copilot-studio-mcp
Advanced tools
MCP server for Microsoft Copilot Studio agent development: workspace sync via pac, YAML authoring (topics, knowledge, tools, flows), evaluations and chat-testing
An MCP server that lets a coding agent (GitHub Copilot in VS Code, Claude Code, or any other MCP client) build, test, ship and look after Microsoft Copilot Studio agents from the editor.
An agent becomes a folder of YAML you can read, diff and commit. The server writes that folder the
way the Copilot Studio VS Code extension does, syncs it with the live agent through the official
Power Platform CLI (pac), and calls the Power Platform, Dataverse, BAP, Power Automate and
DirectLine APIs for what the CLI does not cover: evaluations, chat, cloud flows, portal drift,
transcripts. Nothing reaches a live environment without your approval, and when a choice is still
open the tool asks a question instead of failing.
| You want to | What happens | Read |
|---|---|---|
| Stand up a new agent and get it live | create it inside a solution, generate instructions, add knowledge, topics and tools, review, validate, push, publish, talk to it | Build a new agent |
| Change an agent that already exists | clone it to files, edit or remove components, review, merge what colleagues changed, push | Work on an existing agent |
| Give the agent something to call | connector actions, MCP servers, cloud flows, AI Builder prompts, other agents, chosen from what the environment actually has | Give the agent tools |
| Know whether it works | chat with the published agent, repeatable conversation tests, evaluation runs with per-case results | Publish and test |
| Learn from real users | transcripts, session outcomes, escalation rate, a regression test set built from real questions | Learn from production conversations |
| Not overwrite what a colleague did in the portal | see portal changes since your last sync, block a push that would collide, merge | Keep the workspace and the portal in sync |
| Write a Power Automate flow without hand-writing Logic Apps JSON | compose a definition from steps, create the flow, bind its connections, switch it on, read its run history | Build cloud flows |
| Find out why a flow keeps failing | resolve the real error behind a failed connector action, diff the run against one that worked, and see where the failures concentrate | Build cloud flows |
| Move to test and production | pull the whole solution, map connections and variables, deploy 1:1, publish; or use a pipeline | Ship a solution to another environment |
| Prove the stages match | snapshot each environment, compare, gate a pipeline on drift | Compare environments across DTAP |
| Run the tenant | environments, security roles, DLP, tenant settings, backups to files, onboarding a team, the Microsoft 365 agent catalogue | Administer the tenant |
The complete tool list is in docs/tools.md and the diagrams in
docs/flows.md; both ship
inside the npm package under docs/. The reference sections at the end of this page cover the
agent settings the server can write,
authentication and permissions,
configuration, the hard limits and
how this differs from pac's own MCP server.
Early release. What has actually been exercised:
pac copilot init and pac copilot pack.docs/live-verification.md): the
connector registry, the pac connection list layout, pac copilot clone and its sync metadata,
drift detection and the cs_push conflict refusal, and the evaluation path end to end including
the portal's CSV import format and the metric status strings. A portal-made agent validates
clean, so cs_validate produces no false positives on real content. That run also caught a real
bug: pac copilot publish prints "Failed to publish" and exits 0, so a failed publish was being
reported as a success. Fixed.cs_check_drift in quick mode, cs_chat, the
Dataverse listBots path, moving a solution between environments, and every flow the flow
builder produces (none has been imported into an environment yet).docs/verify.md is the short list of what is still open, docs/STATUS.md the full record. Read
the dry run before confirming anything that writes.
dotnet tool install --global Microsoft.PowerApps.CLI.Tool.
If the SDK lives in your user profile, set DOTNET_ROOT to that folder; the server defaults it
to ~/.dotnet when that folder exists.pac auth create --environment <id or URL>. Everything that goes through pac (create, clone,
pull, push, publish, solutions, administration) uses it, with Microsoft's own first-party app.cs_login, done from the session. By default no app registration is needed;
Authentication and app registration says when you need
one and which permissions it must carry.The server is on npm as copilot-studio-mcp, so the usual install is no install: point your MCP
client at npx, and the first start fetches the package.
There is also a VS Code extension, vscode-extension/, which registers the server with VS Code's
MCP host and exposes the environment variables below as settings. It carries the server and its
dependencies inside the VSIX, so it needs no npm and no network on first start. Build it with
cd vscode-extension && npm install && npm run package; install the resulting .vsix with
code --install-extension copilot-studio-mcp-<version>.vsix.
VS Code, from a terminal or by hand in .vscode/mcp.json (workspace) or the user-level mcp.json:
code --add-mcp '{"name":"copilot-studio","command":"npx","args":["-y","copilot-studio-mcp"]}'
{
"servers": {
"copilot-studio": {
"type": "stdio",
"command": "npx",
"args": ["-y", "copilot-studio-mcp"],
"env": { "CPS_WORKSPACE": "${workspaceFolder}" }
}
}
}
Claude Code (user scope):
claude mcp add-json copilot-studio '{"type":"stdio","command":"npx","args":["-y","copilot-studio-mcp"]}' --scope user
npm install -g copilot-studio-mcp with "command": "copilot-studio-mcp" avoids the npx start-up
cost. The server is published to the MCP Registry as io.github.jgt87/copilot-studio-mcp, which is
what VS Code's MCP gallery (Extensions view, search @mcp) draws from through the GitHub MCP
Registry. To run from a clone instead, see Development.
Environment variables are optional and listed under Configuration.
cs_init. Reports pac and .NET, the pac profiles and which one is active, the MSAL sign-in,
the write policy in force, the workspace it found and the next steps for it. It also returns a
menu of tool presets; on a smaller model pick one (see
Running on a smaller model).cs_login when a cloud tool needs it. The call opens the browser from the server and returns
within 15 seconds; if the sign-in has not finished by then it returns status: pending with the
URL so you can open it yourself, and the next cloud call (or cs_login_status) picks the token
up. That is what makes sign-in work from clients that cap tool-call duration or run the server
where no browser can be launched. Device code (mode: device_code) is the alternative where the
tenant allows it; many block it by Conditional Access policy.cs_guide <topic> when you want the walkthrough for one job rather than inventing a
sequence: getting-started, instructions, knowledge, tools, topics, evaluations,
publish-and-test, drift, transcripts, solutions, administration, troubleshooting.
Each names the tool per step, the portal steps that cannot be automated, and the next steps for
your workspace. Six MCP prompts (new agent, add knowledge, add tool, write instructions, review
and push, check drift) wrap the same walkthroughs in clients that show prompts as commands.The server never changes a live Copilot Studio environment on its own. Every tool that can
(cs_push, cs_publish, cs_run_evaluation, cs_import_solution, cs_deploy_solution,
cs_create_agent with an environment, the delete tools, the flow and admin tools that write, and
the environment-changing pac wrappers) returns a dry run describing what it would do, and does
nothing else, until it is called again with confirm: true. The calling agent is instructed, in the
MCP handshake, to show that dry run and pass confirm only after you agree; one approval covers one
call.
Changing the agent on disk asks the same way, for the same reason: an edit you did not intend is
cheap to make and expensive to notice. cs_add_topic, cs_add_knowledge_source, cs_add_tool,
cs_add_flow, cs_add_trigger, cs_add_variable, cs_edit_topic, cs_edit_tool,
cs_edit_knowledge, cs_update_settings, cs_update_agent and cs_generate_instructions write
nothing on the first call. They return the change instead - a file that would be created shown in
full, a file that would be edited shown as a diff against what is there now - and write it when the
same call is repeated with confirm: true. Reviewing, validating and describing read only, and ask
nothing.
The prose fields get their own accounting, because they are the change a diff alone does not
settle: the preview carries the character count and how much of the product's limit is left, and an
edit that would exceed it is refused outright rather than failing at push time. The limits are not
the same - instructions holds 8000 characters, and the Settings > Responses > "Response
formatting" box (responseInstructions) only 500.
For a hard lock, set CPS_READ_ONLY=1 in the server's environment: the environment-changing tools
are then not registered at all, so no confirmation can reach the environment, while authoring,
validation, review and the read-only tools keep working. cs_init reports the mode and which tools
are withheld. test/policy.test.js fails if a tool that declares confirm is missing from that
list, so the two layers cannot drift apart.
Three more behaviours you will meet in every workflow:
needsInput: true with
what it needs, why, the real choices when the server can list them, and the tool that lists more.
Nothing is written; the calling agent asks you and calls again. This works in every client,
including those without MCP elicitation.cs_pull_solution, cs_create_auth_profile, environment provisioning, every pac
wrapper) accept background: true, return a jobId at once, and cs_job_status reports the
phases and the result from an on-disk record that survives a server restart. A tool decides
whether it may change anything before it starts a job, so the confirm contract is unaffected.Retry-After; a publish or an import that timed out may already have been
applied, so it is reported, not repeated.Making agents and administering the tenant are usually different accounts. pac keeps one active authentication profile per machine, so create one profile per account and let the server switch:
pac auth create --name maker --environment <environment id or url>
pac auth create --name admin --environment <environment id or url>
Or from the session: cs_create_auth_profile with name, environment and background: true,
because pac opens its own browser and waits for the sign-in (cs_job_status reports when the
profile exists). cs_list_auth_profiles shows the profiles and which is active.
Core sync and solution tools use CPS_PAC_PROFILE; admin commands and tenant backup use
CPS_ADMIN_PROFILE; every pac wrapper and cs_pac also accept an explicit profile. When the
server selects a profile it restores the previous one afterwards, and every pac call in the process
shares one queue, so multi-step operations (bootstrap, solution pull and deploy, snapshots) hold
the profile for the whole run. Other server instances and terminal pac commands are outside that
coordination. The MSAL sign-in used by the API-based tools is separate again and independent of pac.
From an empty folder to a published agent you can talk to. Each step is one tool call; the steps that change the environment show a dry run first.
Pick the environment. cs_list_environments (needs cs_login), or take the environment id
from the Copilot Studio URL.
Pick or create the solution. cs_list_solutions shows what exists; an existing solution
works as long as it is unmanaged and you use its publisher prefix. To start a solution for your
agents: cs_create_solution uniqueName=contoso_Agents publisherPrefix=contoso confirm=true.
The solution is the unit you later export and import to move the agent to test and production,
so it is worth choosing rather than letting one be created per agent.
Create the agent inside it.
cs_create_agent name="Contoso Support" publisherPrefix=contoso projectDir=./contoso-support environment=<id> solutionName=contoso_Agents confirm=true (add createSolution=true to fold
step 2 in). The server scaffolds locally with pac copilot init, packs with the solution name,
imports, then clones the live agent back so projectDir is a sync-connected workspace with the
default system topics. Without environment you get a local scaffold only, which can pack
settings, agent and topics but not knowledge, tools or flows.
Leave out publisherPrefix or projectDir and the tool asks instead of guessing, listing the
publishers in the environment (and, when you named a solution, the one prefix that solution's
publisher allows) and suggesting a folder named after the agent. The prefix is worth the
question: it becomes part of the schema name of every component the agent will ever have
(contoso_contososupport.topic.Greeting), it cannot be changed afterwards without recreating
them, and it has to match the publisher of the solution the agent lands in.
Say what the agent is for. cs_generate_instructions purpose="Answer IT questions and create ServiceNow tickets" audience="Employees" tone="Friendly, brief" boundaries=["never reset passwords"] modelName="Agent instructions" drafts the instructions with an AI Builder prompt
(cs_list_prompts shows the prompts in the environment; create a "write agent instructions"
prompt once in AI Builder if you have none). Review the draft, call again with apply=true to
write it into agent.mcs.yml; later refine=true with a changeRequest revises what is
there. Or write them yourself with cs_update_agent, which also sets response instructions and
mode, conversation history, capabilities, moderation, model and conversation starters (the
full map).
Give it something to work with. cs_add_knowledge_source for a public website, SharePoint,
a Graph connector or uploaded files; cs_add_topic for deterministic conversations built from
trigger phrases plus message, question, condition, set-variable, redirect, HTTP, flow, generative
answers (optionally scoped to named knowledge sources), adaptive card, transfer and end nodes;
cs_add_tool for anything the agent should call (the tools workflow).
cs_add_trigger and cs_add_variable cover event triggers and global variables.
Review and validate. cs_review_agent is a rules-based read of the whole workspace with a
10-point score: missing escalation or fallback, weak tool descriptions, overlapping trigger
phrases, private knowledge with no authentication, secrets in files. Each finding names the rule
and the fix. cs_validate then checks every file against the authoring schema (744 definitions)
and across files: connection references that nothing binds, catalog operations that do not
exist, redirects to topics that are not there.
Push. cs_push confirm=true is the portal's Save: the draft agent now shows your topics,
knowledge and tools. The dry run runs validation and the quick drift check first, and the push
is refused when a colleague changed the same component in the portal since your last pull.
Tools with a connection reference need one portal step: open the tool under the agent's Tools,
Connect, then cs_pull to bring the binding down.
Publish and talk to it. cs_publish confirm=true, then cs_chat utterance="my laptop is slow". The testing workflow turns that into repeatable checks.
flowchart TD
A["cs_init"] --> B["cs_list_solutions<br/>pick one, or cs_create_solution"]
B --> C["cs_create_agent<br/>environment + solutionName + confirm"]
C --> D["cs_generate_instructions<br/>AI Builder prompt, then apply"]
D --> E["cs_add_topic / cs_add_knowledge_source / cs_add_tool<br/>(cs_list_connectors, cs_describe_connector)"]
E --> R["cs_review_agent"]
R --> H["cs_validate"]
H -- errors --> E
H -- clean --> I["cs_push confirm"]
I --> J{"tool needs a connection?"}
J -- yes --> J1["portal: authorise once"] --> J2["cs_pull"] --> K
J -- no --> K["cs_publish confirm"]
K --> L["cs_chat"]
GitHub Copilot harness agents (cs_create_agent with authoringMode: cli-copilot) are a smaller
surface: init, pack, import, instructions and chat. Topics and evaluations are standard-harness
features.
Most agents already exist, often made in the portal. The loop is clone, change, review, merge, push.
cs_list_agents (through pac or Dataverse), then cs_clone_agent bot=<id or schema name> outputDir=./support-agent. The result is a sync-connected workspace with
agent.mcs.yml, settings.mcs.yml, topics/, knowledge/, actions/, trigger/,
variables/, workflows/ and the connection references. cs_describe_workspace inventories
it and ends with next steps.cs_edit_topic changes trigger
phrases, priority and nodes (by position or id); cs_edit_tool changes descriptions, inputs and
the connection; cs_edit_knowledge changes a site or a trigger condition;
cs_remove_component deletes a topic, knowledge source, tool, trigger or variable and prunes
the connection reference nothing else uses, noting any redirect left dangling. cs_update_agent
and cs_update_settings cover the agent's own settings. Adding works as in the new-agent flow.
Every edit keeps the file header the extension expects.cs_review_agent, then cs_pull to bring down what colleagues changed in
the portal (pac's three-way merge), then cs_validate.cs_push confirm=true. If a portal change and a local edit touch the same component
the push is blocked; cs_pull resolves it, force: true overrides it.Around that loop: cs_delete_agent and cs_delete_solution remove things from the environment
(confirm); cs_extract_agent_template and cs_create_agent_from_template turn one agent into a
template for more; cs_extract_translations and cs_merge_translations do the localisation round
trip (.resx or .json, with whatIf); cs_quarantine_agent takes an agent out of service and back.
Copilot Studio agents can call any connector in the environment (more than a thousand Microsoft-published ones plus custom connectors), MCP servers exposed through connectors, cloud flows, AI Builder prompts, other agents, and a few rarer kinds. The server knows the kinds from the YAML schema and the instances from the environment, which is the only source that knows what exists there.
cs_list_connectors search=ServiceNow lists the environment's connector
registry, the same list the portal's Add a tool shows, with MCP servers flagged. Without a
sign-in it falls back to an offline seed generated from the public connector reference (display
name to shared_ id, no operations): a starting point, not proof the connector is enabled for
you.cs_describe_connector connector=shared_service-now operation=incident
turns the connector's OpenAPI definition into operations with operationId, required and
optional parameters and response fields; x-ms-agentic-protocol: mcp-streamable-1.0 marks MCP
endpoints. Definitions are cached under .cs-catalog/<environment>/ so later calls are offline.cs_add_tool takes a typed spec for a connector action, an MCP server, a cloud
flow, an AI Builder prompt (cs_list_prompts shows them), a connected agent or a child agent,
and a raw type for the rest (AI plugin, Bot Framework skill, client action, computer-use
agent). With the definition cached it checks the operationId, fills the automatic inputs from
the operation's required parameters, and writes the YAML under actions/ plus a
connection-reference stub. If you have not named a connector or operation yet the call comes
back as a question with the ranked candidates.cs_create_connection makes). After cs_push, open the tool under the agent's Tools, Connect,
then cs_pull. cs_validate warns about tool files whose operation is not in the cached
definition, and cs_review_agent flags tools whose description will not let the orchestrator
pick them.A unit test compares the schema's list of tool kinds with the server's table, so a schema update
that introduces a new kind fails the build until the kind is classified. Flows as tools are either
a scaffold (cs_add_flow, experimental) or a real flow built with the
flow builder and referenced from the agent.
Publish. cs_publish confirm=true is the Publish button, through pac copilot publish or
the Dataverse PvaPublish action polled until publishedon moves.
Chat. cs_chat utterance="..." sends one utterance to the published agent and returns the
reply; pass the conversationId back for the next turn. With transport: auto the server reads
the agent's authentication mode: no-auth and manual-auth agents go through DirectLine (the token
endpoint of a published agent is anonymous, so no app is needed); an agent with integrated Entra
authentication goes through the Copilot Studio client SDK and needs your own app id with the
CopilotStudio.Copilots.Invoke scope (clientId, or CPS_CLIENT_ID).
Make it repeatable. cs_run_conversation_tests runs a YAML file of utterances and
expectations through the same chat path and reports what failed. Expectations cover the
wording and what the agent did: usedTool, notUsedTool, usedTopic, notUsedTopic and
citedKnowledge read the topic, tool and knowledge attribution from the activities, so a test
can tell a real tool call from an answer that merely reads correctly. Every result, and every
cs_chat reply, reports the observed topic, tool and citations; an expectation that cannot be
judged (no attribution in the activities) says so rather than failing the agent. The
attribution key names come from documentation and are unverified live;
docs/test-verification.md is the runbook that settles them.
Evaluate. The portal's Evaluation feature runs a test set of questions against the agent and scores each answer. The one thing its API cannot do is create the test set, so the loop is:
cs_create_test_set_csv suggestFromWorkspace=true writes the CSV in the portal's import
format (max 100 cases), seeded from your topics and knowledge.cs_list_test_sets, then cs_run_evaluation confirm=true wait=true, then
cs_get_evaluation_run for per-case results with pass and fail buckets;
cs_list_evaluation_runs for the history.flowchart TD
A["cs_create_test_set_csv"] --> B["portal: Evaluation > Import CSV"]
B --> C["cs_list_test_sets"] --> D["cs_run_evaluation confirm wait"]
D --> E["cs_get_evaluation_run"] --> F{"failures?"}
F -- yes --> G["fix topics / instructions / knowledge"] --> H["cs_push confirm"] --> D
F -- no --> I["cs_publish confirm"]
Once people use the agent, the transcripts are the best source of test cases and of the questions
it cannot answer. Copilot Studio stores every session in the Dataverse conversationtranscript
table; the server reads it and never writes.
cs_list_transcripts lists sessions in a window: when, how many turns, the first question, the
topics and tools that fired, how it ended.cs_get_transcript returns one session's full turn list with the topic and tool attributed to
each turn.cs_summarize_transcripts aggregates a window: outcomes, escalation rate, sessions that matched
no topic, the top topics and tools, and the questions behind the failures.cs_test_set_from_transcripts builds the Evaluation import CSV from questions people actually
asked, failures first, so the next evaluation run covers what went wrong.Copilot Studio does not report whether a conversation succeeded. The outcome is this server's
reading of the transcript (an escalation, the agent saying it could not answer, a user turn with no
reply); resolved means nothing marked the session as failed, not that the user was satisfied.
Every result repeats that caveat, and cs_guide transcripts walks the loop from a summary to one
transcript to a regression test set. Transcripts hold what users said to the agent, so the
signed-in user needs a role that grants read on that table and results are customer data.
Unverified against a live tenant.
Makers can keep editing an agent in Copilot Studio after it was cloned; nothing stops them and the
platform sends no notification. Every portal edit lands in Dataverse rows: the bot row for
settings and instructions, and one botcomponent row per topic, knowledge source, tool, trigger
and variable, each with a modified-on stamp and the user who changed it. The server uses those rows
to see drift without a re-scan, and a clone to confirm it when the details matter.
cs_clone_agent, cs_pull, cs_push and cs_create_agent (with
environment) write .mcs/cs-sync.json: a fingerprint of every workspace file and, when a
Dataverse sign-in is cached, the modified-on stamp of every component. cs_describe_workspace
shows the last sync.cs_check_drift (default mode: quick) reads the bot row and its component
rows and compares them with the stamp: which components were modified, added or removed in the
portal, by whom and when, whether the agent settings changed, and whether the live agent has
unpublished changes. Each component is mapped to its workspace file; when that file also changed
locally the entry is a conflict. Seconds, no pac; needs cs_login.cs_check_drift mode=full runs pac copilot clone into a temporary folder and
classifies every file three ways against the stamp: local-modified, remote-modified,
both-modified (conflict), added or deleted on either side, with unified diffs. Use it when the
quick check reports drift and you want the exact content, or when there is no Dataverse sign-in
(only the pac profile is needed). Uploaded knowledge files are covered here, not in the quick
check.cs_push dry run includes the quick check, so the caller sees "three
components changed in Copilot Studio since your last pull" before confirming. With confirm,
the push is blocked when a portal change and a local edit touch the same component; cs_pull
(pac's three-way merge) resolves it and force: true overrides it.cs_pull; cs_check_drift reports whether the
workspace is in a repository and how many of its files are uncommitted.Limits. The quick check does not see connections (a maker connecting a tool is expected, and
connection ids are ignored in every comparison), uploaded knowledge files, channel configuration or
the security group. When the stamp has no per-component baseline (no Dataverse sign-in at sync
time) it falls back to the sync time with a two-minute margin, so edits made right after a sync
count as the sync itself. The component query has not been verified against a live environment
yet; see docs/STATUS.md.
Flows are built from a step spec, the way topics are, so you do not have to write Logic Apps JSON
by hand. cs_build_flow_definition composes the definition locally and returns it together with
the connection references it needs; cs_create_flow takes the same spec and creates the flow in an
environment or a solution; cs_update_flow takes it and replaces the definition of an existing
unmanaged flow, keeping its connection references.
A spec is a trigger plus steps that run in order:
| Trigger | For |
|---|---|
agent (default) | the flow a Copilot Studio agent calls as a tool, with typed inputs and a response |
manual, http | started by a person or by an HTTP request |
recurrence | a schedule |
connector | a connector event, such as a Dataverse row being created |
raw | any other trigger, written verbatim |
| Step | Emits |
|---|---|
connector | a connector operation (OpenApiConnection), with its parameters and connection reference |
http | an HTTP call |
condition, foreach, scope | branching, looping and grouping, with their own nested steps |
initializeVariable, setVariable, compose | variables and intermediate values |
terminate | end the run with a status |
response | what an agent-callable or HTTP flow answers with (added automatically when you give outputs) |
raw | any other action, written verbatim |
The builder chains runAfter so each step waits for the previous one, normalises action names the
way Power Automate does, and collects one connection reference per connector used, reusing it
across steps. Expressions are Logic Apps expressions, not Power Fx: @{triggerBody()?['orderId']},
@body('List_rows')?['value']. Connector ids and operation ids come from cs_list_connectors and
cs_describe_connector; an operation's parameters are exactly the parameters of a connector
step.
Around the builder: cs_list_flows shows the flows in the environment with state, owner and
connection references; cs_get_flow returns one with its full definition; cs_delete_flow removes
one for good; cs_list_flow_runs, cs_get_flow_run and cs_run_flow read the run history and
start a manual run through the Power Automate service (a separate sign-in, cs_login scope='flow';
the signed-in user must own or co-own the flow).
Bind the connections. A connection reference names a connector; a connection is one person's
authorised account for it, and until the two are joined the flow cannot run. cs_bind_flow_connection
joins them: with one reference and one usable connection it needs nothing but the flow id, and it
asks which when there is a choice. It writes whichever of the two shapes the flow uses - a flow
built outside a solution names its connection directly, while one that arrived in a solution points
at a connectionreference row - and activate: true turns the flow on in the same call.
cs_set_flow_state turns one on by itself once it is bound.
Work out why a run failed. The run list says a run failed and nothing more.
| Tool | Answers |
|---|---|
cs_explain_flow_run | Why did this run fail? A failed connector action carries no error message of its own - the message is in the action's outputs, behind a link that expires after a few days - so this fetches it, says whether the fault is the connector, an expression or a timeout, and shows the inputs the action was called with next to the outputs of the actions that ran just before it. |
cs_compare_flow_runs | It worked yesterday. Diffs the failed run against the most recent successful one and names the action where they part company. With compareTriggerData: true it reports which keys of the trigger payload differ (key names only, never the values), which is what separates a bad input from a broken flow. Actions present in one run but not the other mean the definition itself changed. |
cs_analyze_flow_health | It fails sometimes. Failure rate, duration median and 90th percentile, and which actions the failures land on. One action responsible for most of them is a broken step; failures spread across many point at the connection, throttling or the system being called. |
A trigger that failed means the flow never ran at all, so the fault is in the trigger's connection or its parameters rather than the logic. Run detail ages out: inputs and outputs are kept for a limited time, so diagnose a failure while it is recent.
Two things the builder cannot do for you. The connections themselves must exist in the target environment before anything can be bound to them, which is why a new flow is created switched off. And the definitions follow the Logic Apps schema and exported solutions rather than a verified round trip, so import one and open it in Power Automate before trusting the shape.
Per-agent sync stays bound to the environment the agent came from. Moving to test or production is a solution export and import, and the server makes that a pull, a settings file and a deploy.
cs_list_solutions, then cs_describe_solution exports, unpacks
and inventories: agents, bot components, flows, connection references, environment variables,
custom connectors.cs_pull_solution exports the solution unmanaged and managed, unpacks it to
src/, writes solution.json (the manifest the deploy reads), creates
deployment-settings.json and clones every agent into agents/. A full pull runs for minutes,
so pass background: true and follow it with cs_job_status; the outcome also lands in
pull-job.json in the target directory.cs_list_connections on the target environment shows the connections that
exist there; cs_create_deployment_settings maps each connection reference to one of them, sets
the environment variable values for the target and, per agent, the Entra security group that may
use it.cs_deploy_solution confirm=true imports into the target with the settings file and
runs pac copilot publish on every agent (publishAgents, default on). It refuses to import
while any connection reference is unmapped unless you pass allowUnmapped: true. If you edited
src/ by hand, cs_pack_solution first.Quality gates and alternatives on the same path: cs_check_solution runs Solution Checker before a
deploy; cs_set_solution_version, cs_solution_online_version and cs_upgrade_solution handle
release numbering and staged upgrades; cs_publish_customizations publishes everything in an
environment; cs_list_pipelines and cs_deploy_pipeline use Power Platform pipelines instead of
a direct import; cs_init_solution_project, cs_clone_solution, cs_sync_solution,
cs_add_solution_reference and cs_add_solution_license keep a source-controlled solution
project (.cdsproj); cs_add_solution_component adds an existing component to a solution.
Plan for these before calling the copy "1:1":
cs_create_connection is the one exception and only covers service-principal Dataverse
connections. With allowUnmapped: true the tools stay unbound until someone binds them in the
target portal.cs_list_flows shows which are not activated and cs_set_flow_state switches
each on; the portal is not needed for that step.CopilotAgents section
with an AadGroupId per agent (verified with pac 2.11.2). Map it to the target's Entra group
(copilotAgents in cs_create_deployment_settings) or set access in the target portal after
import; an all-zero id means no group is set.cs_pull_solution exports both. Deploy managed for downstream
environments and keep unmanaged only for development; a managed import cannot be edited in place
in the target.cs_clone_agent or
cs_pull_solution push back to their source environment only. Edit the agents/<name>
workspace, push to the source, then pull and deploy the solution again.When the same solution is promoted through development, test, acceptance and production, the question is whether each stage still holds what the previous one holds. The platform has no cross-environment diff, but every input is reachable, so the server captures each environment into a snapshot folder and compares snapshots offline.
Snapshot each stage with cs_snapshot_environment (or all at once with
cs_compare_environments, which takes an ordered chain and compares each adjacent pair). A
snapshot folder contains:
snapshots/TEST/
snapshot.json label, environment, time, solution version and managed flag,
agents with publish state, flows, connection references,
environment variables, notes
agents/<Agent name>/ the agent as YAML, from pac copilot clone
Agents are cloned rather than exported as a solution, because a managed solution cannot be
exported from test or production. Flows, connection references, environment variables and
publish state come from Dataverse and are included when you are signed in with cs_login;
otherwise the snapshot notes that they were skipped.
Compare adjacent stages with cs_compare_snapshots (DEV vs TEST, TEST vs ACC, ACC vs PROD).
Each comparison writes <A>-vs-<B>.md and .json.
Read the verdict. A report starts with DRIFT or no drift, then lists the drift, the
expected differences, and tables per layer with unified diffs for changed agent files.
Act on it. Drift in agent YAML means the later stage is behind or was edited in place: promote again with the solution flow above. Unbound connection references, missing flows, or variables without a value are deployment-settings problems: fix the settings file and redeploy.
Keep history. Snapshot folders are plain files; commit them (without .mcs/ state) to get
a timeline per stage.
What counts as drift, and what is an expected difference between stages:
| Layer | Drift | Expected difference (reported, not drift) |
|---|---|---|
| Solution | version differs, missing in a stage | managed in later stages, unmanaged in development |
| Agent YAML (topics, instructions, knowledge, tools, triggers, variables) | any changed, added or removed file after normalisation | ids, audit info, version fields, connection ids inside connectionreferences.mcs.yml (removed before diffing) |
| Publish state | modified after last publish in any stage (unpublished changes) | |
| Flows | missing in a stage, on/off state differs | |
| Connection references | missing, connector differs, unbound in a stage | bound to a different connection per stage |
| Environment variables | missing, no value in a stage | different values per stage (drift only with strictVariables) |
| Authentication mode | differs between stages |
Pipeline gate. cs_compare_snapshots and cs_compare_environments accept failOnDrift; the
tool result is then an error, which a scripted MCP client or a pipeline step can turn into a failed
job. A typical gate before promoting TEST to ACC:
cs_snapshot_environment label=TEST environment=<test id> dir=snapshots/TEST solution=<name>
cs_snapshot_environment label=ACC environment=<acc id> dir=snapshots/ACC solution=<name>
cs_compare_snapshots a=snapshots/TEST b=snapshots/ACC failOnDrift=true
Caveats: all stages must be reachable from the active pac auth profile (for chains that span
tenants, run pac auth select between stages and snapshot them one by one); whether
pac copilot clone succeeds on a managed agent is unverified live (the fallback is reading the same
component definitions through the Dataverse Web API); uploaded knowledge files and channel
configuration are listed by name and size only; without a Dataverse sign-in the comparison covers
solution version and agent YAML only, and the report says so.
Administration runs as the admin account (two accounts): pass
profile, or set CPS_ADMIN_PROFILE once. Every change goes through the same dry run and
confirm; reset, delete, copy and restore destroy or overwrite whole environments, and the dry run
says what will be lost.
Back the configuration up to files. cs_backup_tenant writes the whole tenant configuration
to a folder: tenant settings, environments, DLP policies, environment groups, service principals,
registered applications, templates, and per environment its details, solutions, agents,
connections, security roles and platform backups. Raw output is kept next to parsed rows, and a
capture that fails is isolated so the rest of the backup still lands.
Read and change the tenant. Environments and their operations in progress, tenant settings
(read to a JSON file, change one), DLP policies (which connectors may be combined), environment
groups, security roles, service principals and registered applications, managed-environment
governance, administration mode and backup retention, and the environment lifecycle: create,
copy, back up, restore, reset, delete. Each is one cs_admin_* tool
(list).
Onboard a team into a new environment. cs_admin_create_environment (slow; pair it with
background: true and cs_job_status), then cs_admin_assign_users with a CSV:
UPN,Security Roles,Business Unit
alice@contoso.com,"System Customizer,Basic User",Sales
bob@contoso.com,Environment Maker,
pac takes one user and one role per call, so that roster is five calls; the tool expands it,
shows you every user-and-role pair in the dry run, and runs them under one approval. Rows are
independent, so a mistyped UPN is reported and the rest still run. Prefer
cs_admin_assign_group when the roster is really a group: binding an Entra group to a role is
one call however many people are in it, and new joiners inherit access. Role names are
per-environment (cs_admin_list_security_roles against the new one), and a user who has not
been provisioned into the environment yet cannot hold a role there.
The Microsoft 365 agent catalogue. cs_list_agents reads one environment's bots table and
stops at the Power Platform boundary. The catalogue (Microsoft Graph, a separate sign-in with
cs_login scope='graph') is tenant-wide and answers the question that follows cs_publish: did
the agent actually reach anyone? cs_list_org_agents reports who each agent is available to,
where it is deployed and whether it is blocked, filtered by platform, host, element type or
last-modified date; cs_get_org_agent returns one entry in full; cs_block_org_agent blocks an
agent for everyone in the tenant or lifts the block, and cs_reassign_org_agent hands it to a
new owner when the old one leaves (both confirm). Needs a Microsoft Agent 365 licence, is
global-cloud only, is unverified against a live tenant, and block and reassign exist only on
Graph beta.
Every pac command without a tool of its own is reachable through cs_pac: read-only commands run
immediately, the others return a dry run and need confirm. The pac groups outside Copilot Studio
work (application, canvas, catalog, code, data, managed-identity, model, modelbuilder, package,
pages, pcf, plugin, power-fx, telemetry, test, tool) are deliberately left there.
An agent's settings live in two files, and the server reads, validates and pushes both. The table
maps what the portal shows to the field and the tool argument that writes it. Everything here is a
local file change, shown before it is made and written only with confirm: true; cs_push applies
it, and cs_publish makes it live. instructions is capped at 8000 characters by the product, and
the preview counts them.
agent.mcs.yml (the agent definition), written by cs_update_agent:
| Portal | Field | Argument |
|---|---|---|
| Instructions | instructions | instructions, appendInstructions (or cs_generate_instructions) |
| Responses: how answers are worded and formatted ("Response formatting") | responseInstructions | responseInstructions, appendResponseInstructions - 500 characters, counted in the preview |
| Responses: response mode | defaultResponseMode | defaultResponseMode: Auto, ThinkDeeper, QuickResponse |
| Conversation history the agent sees | historyType | history: none or conversation, with historyMessages |
| Capabilities: web browsing, code interpreter, image generation, Teams / SharePoint / email / meeting / people search | gptCapabilities | capabilities (only the toggles you pass change) |
| General knowledge: may the model answer beyond your knowledge sources | aISettings.useModelKnowledge | useModelKnowledge |
| Content moderation | aISettings.contentModeration | contentModeration: Minimum to Maximum |
| File analysis, semantic search | aISettings.isFileAnalysisEnabled, aISettings.isSemanticSearchEnabled | isFileAnalysisEnabled, isSemanticSearchEnabled |
| Model | aISettings.model.modelNameHint | modelNameHint |
| Conversation starters | conversationStarters | conversationStarters, addConversationStarters |
| Display name | displayName | displayName |
Values are checked against the authoring schema, so a response mode or moderation level outside the allowed set is refused rather than written.
settings.mcs.yml (how the agent runs), written by cs_update_settings with dot paths, for
example {"configuration.settings.GenerativeActionsEnabled": true}:
| Portal | Path |
|---|---|
| Generative orchestration on or off | configuration.settings.GenerativeActionsEnabled |
| Authentication | authenticationMode (None, Integrated, Manual), authenticationTrigger, accessControlPolicy |
| Language | language |
| Agent can be called by other agents | configuration.isAgentConnectable |
| Analytics, telephony, voice, network | configuration.analyticsSettings, isTelephonyEnabled, botSpeechSettings, networkSettings |
cs_update_settings refuses to change authoringModel, recognizer.kind and template,
because the tooling depends on them. cs_lookup_schema shows any other field the schema allows,
and cs_validate checks whatever you write by hand.
One caveat: these field names come from the authoring schema, not from a round trip through a live
agent, so which portal control maps to which field is inference. Set one in the portal, run
cs_clone_agent and compare if you need certainty.
The server signs users in with an MSAL public client (interactive browser or device code). It
never uses client secrets, so every permission it needs is a delegated permission acting as the
signed-in user. Application permissions are listed below only where Microsoft offers them, for
people who build a headless pipeline on top of the same APIs. How cs_login behaves inside an MCP
client is described under The first session.
Do you need your own app registration?
| Situation | App registration needed? |
|---|---|
pac commands (cs_create_agent, cs_clone_agent, cs_pull, cs_push, cs_pack, cs_publish via pac, cs_check_drift in full mode, all cs_*_solution tools, and every pac wrapper) | No. pac auth create signs in with Microsoft's own first-party app. |
Cloud tools with the default client id (cs_list_environments, cs_list_agents via Dataverse, cs_publish via Dataverse, cs_check_drift, evaluations, cs_chat for no-auth or manual-auth agents) | No. The server uses the first-party VS Code client id 51f81489-12ee-4a9e-aaae-a2591f45987d, which is pre-authorised for Power Platform API, Dataverse and the Power Apps service. Microsoft's own Copilot Studio tooling uses the same id. |
| Same tools, but your tenant blocks that id (app consent policy, conditional access, "user assignment required") | Yes. Create the registration below and set CPS_CLIENT_ID. |
cs_chat with an agent that uses integrated authentication (Entra SSO) | Yes, always. The first-party id does not carry CopilotStudio.Copilots.Invoke for third parties. Pass clientId to cs_chat or set CPS_CLIENT_ID. |
| Headless CI (service principal, no user) | Not supported by this server today (public client only). For pipelines use pac auth create --applicationId ... --clientSecret ... with a Dataverse application user, or the Power Platform API with an RBAC role assigned to the service principal. |
Permissions for your own app registration (Entra ID > App registrations > API permissions > "APIs my organization uses"):
| Used by | API to pick in Entra | Permission | Delegated or application | Notes |
|---|---|---|---|---|
cs_chat (transport sdk, Entra-SSO agents) | Power Platform API (app id 8578e004-a5c6-46e7-913e-12f58912df43) | CopilotStudio.Copilots.Invoke | Delegated is what this server uses. An application permission of the same name exists for confidential clients (Microsoft 365 Agents SDK); not used here. | Admin consent is normally required. Redirect URI http://localhost (Mobile and desktop applications). |
cs_list_test_sets, cs_run_evaluation, cs_get_evaluation_run, cs_list_evaluation_runs | Power Platform API | Token scope https://api.powerplatform.com/.default. The evaluation endpoints declare only .default; the permission reference has no finer-grained evaluation permission. | Delegated only. Power Platform API has no application permissions; service principals get access through RBAC roles instead. | Verified with the first-party id by Microsoft's own tooling. Not yet verified with a custom registration; if calls return 403, the signed-in user needs maker access to the agent. |
cs_list_environments, automatic Dataverse URL lookup, cs_list_connectors, cs_describe_connector, the connection lookup in cs_bind_flow_connection | PowerApps Service (app id 475226c6-020e-4fb2-8a90-7a972cbfc1d4) | User ("Access the Power Apps Service API") | Delegated. | Calls the BAP environments API (api.bap.microsoft.com), the connector registry and the connections list (api.powerapps.com). Listing connections shows every one in the environment the signed-in user can see, including other people's; binding one makes the flow run as that connection's owner. |
cs_list_agents (via dataverse), cs_publish (via dataverse), the publisher and solution lookup that cs_create_agent offers as choices, authentication-mode detection in cs_chat, cs_check_drift (quick mode), the drift preflight in cs_push, the component stamps recorded by cs_clone_agent / cs_pull / cs_push, the flow tools (cs_list_flows, cs_get_flow, cs_create_flow, cs_update_flow, cs_set_flow_state, cs_delete_flow, and the Dataverse half of cs_bind_flow_connection) and the transcript tools (cs_list_transcripts, cs_get_transcript, cs_summarize_transcripts, cs_test_set_from_transcripts) | Dynamics CRM (Dataverse, app id 00000007-0000-0000-c000-000000000000) | user_impersonation | Delegated. There is no application permission; server-to-server access to Dataverse means an application user with a security role in each environment. | The user still needs a Dataverse security role that can read and publish bots (System Customizer or a Copilot Studio maker role). The drift checks only read the bot and botcomponent tables, which every maker role can read; they never write to Dataverse. cs_create_agent reads publishers (and the publisher of a named solution) only to offer real prefixes instead of a guess; failing that read costs the choices, not the tool. The transcript tools read conversationtranscript, which holds what users actually said to the agent: a more sensitive table than the rest, so the signed-in user needs a role that grants read on it, and results should be treated as customer data. When a workspace has no Dataverse URL in its sync metadata the URL is looked up through the PowerApps Service permission above. |
cs_list_flow_runs, cs_get_flow_run, cs_run_flow, cs_explain_flow_run, cs_compare_flow_runs, cs_analyze_flow_health | Microsoft Flow (Power Automate service, app id 7df0a125-d3be-4c96-aa54-591f83ff541c) | User (token scope https://service.flow.microsoft.com/.default, override with CPS_FLOW_SCOPE) | Delegated. | A separate resource from Dataverse and the Power Platform API, so it needs its own consent: sign in with cs_login scope='flow'. The signed-in user must be an owner or co-owner of the flow. Endpoints and scope are taken from the service the Power Automate portal calls and are unverified against a tenant. The diagnostic tools also follow the SAS-signed content links the service returns for an action's inputs and outputs; those are fetched without the bearer token, because they carry their own signature, and they expire after a few days. |
cs_list_org_agents, cs_get_org_agent | Microsoft Graph (app id 00000003-0000-0000-c000-000000000000) | CopilotPackages.Read.All (override the scope with CPS_GRAPH_SCOPE) | Delegated is what this server uses; an application permission of the same name exists for reads. | The Microsoft 365 agent catalogue, a different resource again: sign in with cs_login scope='graph'. Requires a Microsoft Agent 365 licence and is global-cloud only (no GCC, DoD or 21Vianet). Reads /v1.0/copilot/admin/catalog/packages. Unverified against a live tenant. |
cs_block_org_agent, cs_reassign_org_agent | Microsoft Graph | CopilotPackages.ReadWrite.All (override with CPS_GRAPH_WRITE_SCOPE) | Delegated only. Microsoft offers no application permission for these two actions. | Same licence and cloud limits as above; sign in with cs_login scope='graph_write'. These exist only on /beta, so they are pinned there whatever version a read used. Blocking is tenant-wide and takes effect for every user at once. Unverified against a live tenant. |
cs_chat for no-auth or manual-auth agents (DirectLine) | none | none | not applicable | The DirectLine token endpoint of a published agent is anonymous. |
pac interactive sign-in | none | none | not applicable | Microsoft's own app; pac auth create --environment <id>. |
Steps for your own registration:
http://localhost (the loopback
MSAL uses for cs_login interactive). Set Allow public client flows to Yes if you plan to use
cs_login with mode: device_code.az ad sp create --id 8578e004-a5c6-46e7-913e-12f58912df43 (or
New-MgServicePrincipal -AppId 8578e004-a5c6-46e7-913e-12f58912df43) and search again.CPS_CLIENT_ID=<application (client) id> and
CPS_TENANT_ID=<directory (tenant) id>. cs_chat also accepts clientId per call.All environment variables are optional:
| Variable | What it does |
|---|---|
CPS_WORKSPACE | the agent workspace to start in (VS Code: ${workspaceFolder}); otherwise the server looks for marker files in the current directory, a single child, or an ancestor |
CPS_TENANT_ID, CPS_CLIENT_ID | the Entra tenant and app registration for cs_login; default tenant organizations, default client the first-party VS Code id |
CPS_ENVIRONMENT_ID, CPS_ENVIRONMENT_URL, CPS_AGENT_ID | defaults for cloud tools when the workspace carries no sync metadata; explicit arguments win, then workspace metadata, then these, then a BAP lookup |
CPS_PAC_PROFILE, CPS_ADMIN_PROFILE | the pac auth profile for maker commands and for admin commands (two accounts) |
CPS_READ_ONLY | withhold every environment-changing tool (approval) |
CPS_TOOLS, CPS_TOOLS_EXCLUDE | trim the tool list: presets and comma-separated names with * wildcards (below) |
CPS_FLOW_SCOPE, CPS_GRAPH_SCOPE, CPS_GRAPH_WRITE_SCOPE | override the token scopes for the Power Automate service and Microsoft Graph |
CPS_CACHE_DIR, PAC_PATH, DOTNET_ROOT | token cache location, the pac executable, the .NET root |
The full tool list is 142 tools, about 50k tokens of schema before any work starts. A frontier
model copes; a smaller one spends most of its context on the menu and chooses worse from it. Set
CPS_TOOLS to a preset in the server's environment, or let the user choose during the session:
cs_init returns a toolPresets block with the question, the option table and a live count per
preset, and cs_set_tool_preset applies the answer.
| Preset | Tools | When to use it |
|---|---|---|
full (the default) | 142 | everything; a large model, or you do not know yet what the task needs |
core | 34 | build or change one agent and get it live: the usual choice |
authoring | 25 | write and check files only; no sign-in, nothing reaches an environment |
admin | 44 | tenant administration as the admin account, plus cs_backup_tenant |
solutions | 25 | pull, deploy and compare solutions, and bind the flows an import left switched off |
Nothing is removed: a preset only changes which tools are offered, and cs_init, cs_guide,
cs_set_tool_preset and cs_job_status survive every preset so a session can always change its
mind. cs_pac is in core, admin and solutions so any pac command a preset hides is still
reachable, and cs_init says that a missing tool is hidden rather than absent. Presets compose
with each other and with globs: CPS_TOOLS=core,cs_admin_*, or
CPS_TOOLS_EXCLUDE=cs_*_pipeline,cs_env_*,cs_*_auth_profile. The SDK sends
notifications/tools/list_changed on a switch, so a client that honours it sees the shorter list
at once; one that caches the list needs a restart. Runtime switching can only narrow what was
registered at startup, so leave CPS_TOOLS unset if you want every preset available to choose
from.
A tool list this long is a routing problem: the descriptions decide whether "why did my flow fail
last night?" reaches cs_explain_flow_run or something that answers a different question. The
package ships the check, so you can measure it against your own preset, your own model and your
own phrasings rather than trusting the numbers here:
npx copilot-studio-mcp-routing-eval --dry-run # free: shows the corpus and the cost
npx copilot-studio-mcp-routing-eval --preset core --repeat 3 --yes
npx copilot-studio-mcp-routing-eval --cases ./my-questions.json --yes
It puts the tool list in front of a model exactly as your client sees it, one utterance at a time,
and scores the first tool named. --cases takes your own file in the shape of the bundled
reference/routing-cases.json, which is rather the point: the questions your users actually ask
are better evidence than the ones shipped here. With ANTHROPIC_API_KEY set it calls the Messages
API with the tool list as a cached prefix and a run costs cents; without one it falls back to the
claude CLI and the sign-in that machine already has. Either way a run calls a model once per
case and spends real money, so nothing happens without --yes.
Read it with --repeat 3, never a single run. Measured on Haiku 4.5, the score moves by two
cases between runs of an identical build, and a third of the cases answer differently each time.
One run will happily tell you a change helped when it did not. The report separates the cases that
are always misrouted, which are worth acting on, from the ones that flap, which prove nothing.
The handshake instructions are written for a smaller model: one rule per line, an explicit trigger before each instruction, and a section naming the failures seen in the first live run (a call cut off at the client's ~60s limit, a tool missing because the server binary was stale, a cloud tool failing for want of a sign-in, pac reporting failure while exiting 0) with the action for each.
| Capability | How | Status |
|---|---|---|
| Agent as files, sync both ways | pac copilot init / clone / pull / push / pack / publish | official, GA |
| Topics, knowledge, tools, triggers, flows as YAML | workspace layout of the VS Code extension and pac copilot push | official |
| Local validation | JSON schema from microsoft/skills-for-copilot-studio (MIT) + structural checks | this server |
| Run evaluations, read results | Power Platform API makerevaluation endpoints | official, GA, standard harness |
| Chat with the published agent | DirectLine v3 (no-auth / manual-auth agents) or Copilot Studio client SDK (Entra SSO) | official |
| Publish | pac copilot publish or Dataverse PvaPublish | official |
| Read, create, change and delete cloud flows | Dataverse workflow rows (definition in clientdata) | official API, shapes unverified live |
| Bind a flow's connections | clientdata connection references, or the connectionreference row for a flow that came in a solution | official API, shapes unverified live |
| Flow run history, start a run | Power Automate Process Simple API | the service the portal calls, unverified live |
| Explain a failed run, diff it against one that worked, score a flow's reliability | the same API's per-run actions route, plus the SAS-signed content links it returns | this server, unverified live |
| Portal drift since the last sync | bot and botcomponent rows against a local sync stamp | this server |
| Tenant configuration to files | pac admin read commands into a folder | this server |
Hard limits the server works around rather than hides:
cs_create_test_set_csv writes
the CSV the portal imports (max 100 cases); after one import, runs and results are automated.cs_add_tool writes the YAML and the connection-reference stub and returns the portal step.
The one exception is a service-principal Dataverse connection, which cs_create_connection
can create. The same limit is why a new cloud flow is created switched off: bind its
connections, then cs_set_flow_state.--authoring-mode cli-copilot) get init / pack / import / instructions / chat only.pac copilot pack on a workspace from pac copilot init without --environment
packages settings, agent and topics only and rejects knowledge/, actions/, tools/,
trigger/, variables/, workflows/ and connectionreferences.mcs.yml. Those folders are
handled by pac copilot push from a sync-connected workspace (clone, or init with an
environment). The authoring tools tell you when you are in a pack-only workspace.The Power Platform CLI ships a built-in MCP server (pac copilot mcp --run, preview, named
"Power Platform Management MCP Server"). It is a natural-language front end to pac itself: each
of its tools runs one pac command. Probed on pac 2.11.2 it exposes 70 tools, of which exactly one
is about Copilot Studio (copilot_publish); 51 are tenant administration, managed identity,
model-driven apps and generated pages, Power Pages, code apps and code generation.
pac MCP server (pac copilot mcp) | copilot-studio-mcp (this repo) | |
|---|---|---|
| Purpose | run pac commands in natural language; tenant and environment administration | build, test, ship and maintain Copilot Studio agents from the editor |
| Copilot Studio commands | copilot_publish only | init, clone, pull, push, pack, publish, status, list, delete, templates, translations, quarantine, AI Builder instructions |
| Authoring | none | topics, knowledge sources, tools (connector / MCP / flow / prompt / agent), triggers, variables, and the agent's own settings as YAML; day-two edit and remove; schema validation (744 definitions); rules-based review with a score |
| Testing | none | evaluation test sets and runs (Power Platform API), chat through DirectLine or the client SDK, local conversation tests, production transcripts |
| ALM | solution list, export, import, check | pull a whole solution with a deployment settings file and redeploy it 1:1, Solution Checker, versioning, staged upgrades, pipelines, DTAP snapshots and comparison |
| Drift | none | portal changes since the last sync (quick Dataverse check, full clone diff) and a push preflight that blocks on conflicts |
| Safety | no dry run in the server; the client's approval prompt is the only gate | every environment-changing tool returns a dry run until confirm: true; secrets are masked in logs and results; CPS_READ_ONLY withholds the writers entirely |
| Tenant administration | admin commands as native tools | the admin commands plus cs_backup_tenant and roster onboarding, run as a separate admin auth profile |
| Cloud flows | none beyond raw pac | list, read, create, rebuild, switch on or off, run history, and a step-based definition builder |
| Guidance | tool descriptions | usage instructions in the handshake, cs_guide walkthroughs, MCP prompts, next steps per workspace, and questions with real choices instead of argument errors |
| Other pac groups | managed-identity, model, pages, code, modelbuilder as native tools | reachable through cs_pac (read-only commands run immediately, others need confirm) |
| Sign-in | pac auth profile | pac auth profile, plus MSAL for the APIs pac does not cover (evaluations, Dataverse reads, environments, chat) |
| Tool list | fixed | presets and CPS_TOOLS / CPS_TOOLS_EXCLUDE trim it per client |
Both are stdio servers and can be registered side by side: pac's for tenant administration, this one for agent work. One practical note: pac's server prints a non-JSON line on stdout at startup, which strict MCP clients may reject.
Sources: Use Power Platform CLI with built-in MCP server
and a tools/list probe of pac 2.11.2 (2026-09-07).
flowchart LR
VS["VS Code / GitHub Copilot"] --> TOOLS
CC["Claude Code"] --> TOOLS
subgraph SERVER["copilot-studio-mcp"]
TOOLS["MCP tool call"] --> SYNC["Sync layer: pac copilot, pac solution, pac admin"]
TOOLS --> AUTH["Authoring layer: YAML + schema validation, flow builder"]
TOOLS --> CLOUD["Cloud layer: evaluations, publish, chat, flows, drift"]
end
AUTH --> WS["Agent workspace on disk: topics, knowledge, actions, workflows"]
SYNC --> WS
SYNC --> DV["Dataverse / Copilot Studio"]
SYNC --> BACKUP["Tenant backup and solution folders on disk"]
CLOUD --> PPAPI["Power Platform API"]
CLOUD --> DV
CLOUD --> FLOW["Power Automate service: flow runs"]
CLOUD --> DL["Published agent: DirectLine or SDK"]
The cloud workflows were built from documentation and the published schema; phases A to F
received a first live verification on 2026-09-08. docs/verify.md is the current follow-up list.
docs/live-verification.md is the full runbook: phases A to D are read-only and retire most of the
open questions without touching the tenant, E to G write and say so. docs/verification-template.md
is the results file to fill in, and scripts/redact-verification.mjs replaces GUIDs, org URLs,
emails and tokens with stable pseudonyms so a result can be shared from a public repo.
docs/STATUS.md is the full historical record. docs/test-verification.md is the runbook for the
feedback loop (static checks, behavioural tests, transcripts, closing the loop); its phase 1
settles whether live activities carry the topic, tool and citation attribution the conversation
tests read.
git clone https://github.com/jgt87/copilot-studio-mcp.git
cd copilot-studio-mcp
npm install
npm run build # dist/index.js is the server: register it as "command": "node", "args": ["<repo>/dist/index.js"]
npm test # build + unit tests (fixtures, no network)
node scripts/smoke.mjs # drive the built server over stdio
node scripts/oracle-pack.mjs # pac copilot init + authoring tools + pac copilot pack
reference/bot.schema.yaml-authoring.json and reference/templates come from
microsoft/skills-for-copilot-studio (MIT, see reference/LICENSE.skills-for-copilot-studio.txt).
Fixtures under test/fixtures/pac-* were generated with pac copilot init.
MIT licensed.
FAQs
MCP server for Microsoft Copilot Studio agent development: workspace sync via pac, YAML authoring (topics, knowledge, tools, flows), evaluations and chat-testing
The npm package copilot-studio-mcp receives a total of 0 weekly downloads. As such, copilot-studio-mcp popularity was classified as not popular.
We found that copilot-studio-mcp demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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