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agentic-os-mcp

MCP server serving the agentic-os governance, SDLC, and QE methodology. Read-only.

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agentic-os-mcp

npm npm downloads License: Apache-2.0 Glama MCP server

Read-only MCP server for the agentic-os methodology: governance (agentic-os), the SDLC pipeline (agentic-sdlc), and Quality Engineering blueprints (agentic-qe).

What this is, in plain words

agentic-os puts guardrails on AI coding agents — what files they may touch, when they must stop and ask a human, and how their work gets independently reviewed before anything is committed. On top of that, agentic-sdlc is an optional pipeline that carries a ticket from idea → spec → plan → tested code → review-ready pull request, and agentic-qe is a catalog of Quality-Engineering blueprints. There are role setups for developers, QA, architects, DevOps, business analysts, and project/portfolio managers.

This MCP server hands that whole methodology — the docs, a per-role install plan, the QE catalog, and an install auditor — to any MCP-capable assistant, not just the Claude Code / Cursor plugins. So you can explore agentic-os, plan an install, or audit an existing one from wherever your assistant runs.

It serves knowledge, not actions. Every tool is read-only — the server never writes to your repository. It gives your assistant the documents and the plan; your assistant performs any file writes, so you review each one. To actually run the governed pipeline or scaffold files into a repo, install the plugins (see the main README).

Install

Claude Code:

claude mcp add agentic-os -- npx -y agentic-os-mcp

Codex:

codex mcp add agentic-os -- npx -y agentic-os-mcp

VS Code — one-click:

Install in VS Code

or from the command line:

code --add-mcp '{"name":"agentic-os","command":"npx","args":["-y","agentic-os-mcp"]}'

Cursor — one-click:

Add to Cursor

or add to .cursor/mcp.json directly, same as Claude Desktop's claude_desktop_config.json:

{ "mcpServers": { "agentic-os": { "command": "npx", "args": ["-y", "agentic-os-mcp"] } } }

How you use it

You don't call these tools yourself — your assistant does, when you ask in plain language. A typical exchange:

You: "Search the agentic-os methodology for how to run a code review."

Assistant: calls search_methodology → gets ranked agentic-os:// URIs → calls get_document on the top hit → summarizes the code-review skill for you.

Start with search_methodology (find the right document) and get_document (read it). The other five tools list the presets, phases, and blueprints, turn a role into an install plan, or audit an existing install — see the examples below.

Examples by role

Real things to ask your assistant once the server is connected. Each role is a preset; list_presets shows all ten with their human-in-the-loop mode.

RoleAsk your assistantTools it triggersWhat you get
Developer"Walk me through the agentic-sdlc pipeline, then plan a developer install."list_sdlc_phases, plan_installthe 13-phase spec → plan → TDD → review → PR map, plus an ordered file manifest (stack-aware generator agents, blind + security review, QA gates)
QA"What QE blueprints do you have for the execution stage?"list_qe_blueprintsthe QE catalog (28 blueprints across 6 STLC stages) — e.g. coverage-analysis, flaky-debugging — to scaffold a test framework from
Architect"How does the blind pre-commit review gate work?"search_methodologyget_documentthe governance doc itself — the instruction-quality rubric, review gates, and patterns
DevOps"What does the devops role set up, and how does MR-watch fix CI?"list_presets, get_documentthe devops preset (git hooks, PR pipeline gate) and the mr-watch skill that monitors merge requests and auto-fixes CI until they merge
BA / PO"How do I turn a ticket into requirements with agentic-sdlc?"search_methodology, get_documentthe requirements-intake / product-owner method — a story with acceptance criteria and an early complexity read (no code)
PM / delivery"What's the PR-gate and status workflow for a delivery manager?"list_presets, get_documentthe pm-delivery preset — ticket/MR adapters, a PR pipeline gate, mr-watch, and status conventions
Portfolio"What cross-project oversight does the portfolio role give?"list_presets, get_documentrun status, repo/knowledge-health audits, and durable cross-session memory — read/report-only, no git footprint
Security"What does the security preset enforce when I threat-model a feature?"list_presets, get_documentthe threat-modeler contract + threat-modeling guide — DFD-first STRIDE with per-element constraints, severities proposed — owner confirmation pending, writes only docs/security/
Data"What are the standards for designing a pipeline with quality checks?"list_presets, get_documentthe pipeline-designer contract + data-pipeline-design guide — counted row-math equations, force-tested DQ checks, lineage, recommend-only queries
Design"What must an agent-ready design handoff contain?"list_presets, get_documentthe experience-designer contract + experience-design guide — emotion-annotated journeys, decision-closing workshops, verbatim negative ACs, the context+spec pair

plan_install turns any of these roles into an ordered file manifest; you (or your assistant, with your review) apply it. To run the pipeline or scaffold the files for real, install the plugins — see the main README.

Tools

ToolPurpose
search_methodologyFind the right document. Start here.
get_documentFetch one document by its agentic-os:// URI.
list_presetsList the agentic-os role presets with HITL default, orchestration mode, and SDLC skills.
list_qe_blueprintsList the agentic-qe Quality Engineering blueprints, filterable by STLC stage.
list_sdlc_phasesList the agentic-sdlc pipeline phase map with its judgment gates.
plan_installCompose one or more role presets into an ordered file manifest (a plan; you perform the writes).
run_doctorAudit an agentic-os install in a target repo you name.

The tool surface is deliberately capped at 8 tools. Agent tool-selection accuracy degrades sharply as the number of available tools grows — research shows the performance cliff occurs around 30–40 tools, and real deployments (e.g., GitHub's MCP server) report measurable gains (2–5 points on SWE-Lancer and SWEbench-Verified, plus ~400ms lower latency) after cutting from ~40 tools to 13. The current surface exposes seven tools, one short of the cap.

run_doctor's split verdict

run_doctor audits .agentic/agentic-os/install.json and the files it journals in a target repo you name, through a reader (mcp/src/target.ts) gated by root containment rather than the bundle's build-time index — see SECURITY.md for exactly how that gate works and the one accepted risk it documents. It verdicts what it can inspect natively as plain file reads, and returns everything else as host_must_run: exact commands for three checks that require executing Python (hook compile+import, canned-event dry-runs, HITL smoke) — the server never runs them itself.

Because of that split, verdict: "incomplete" is the expected, correct result of a server-side-only run, not a sign that something went wrong. verdict is "passed" only when every native check passed and host_must_run is empty (i.e., your host actually ran the returned commands and folded their results back in); it is "incomplete" whenever host_must_run still has entries, and "failed" only when a native check itself found a real problem. A reader who sees "incomplete" on its own should read it as "native checks passed; three checks are still owed to the host," not as a failure. host_must_run is never empty on a single server-side call, so this server alone never returns "passed".

A host that automatically runs host_must_run's commands writes to the target repo, even though the server itself never does. Two of the three command sets do: dry_runs creates .agentic/agents/__agentic_doctor_probe__.md (a one-line dummy contract, to exercise instruction_gate.py's never-graded case) and deletes it in the next command, unconditionally; hitl_smoke creates a temporary working directory outside the target repo (via mktemp -d) holding synthetic transcript files, removed automatically on exit by a shell trap. Both are the doctor's real, documented procedure — nothing here is left behind — but it means the "read-only" claim above is a property of this server's own code, not of what happens if a host executes what run_doctor hands back. See each host_must_run entry's why field for the exact commands.

Resources

31 agentic-os://skills/<plugin>/<skill> resources, one per SKILL.md across the three plugins, plus a resource template, agentic-os://file/{+path}, that serves any other file shipped by a plugin that is tracked in content-index.json — not just markdown, JSON, or text: template sources (.md.tmpl/.json.tmpl/.py.tmpl), plain hook scripts (.py/.sh and six extensionless git hooks), and a long tail of one-off files (sdlc.html, scaffold.ps1, run-hook.cmd, .ts/.mts sources, .shellcheckrc, *.md.template repo-guide templates) are all servable too (e.g. agentic-os://file/agentic-sdlc/agents/guide-sync.md). Index membership is the entire access-control model — see mcp/src/content.ts. The template is the primary integration point for clients that want to reach content beyond the curated skill list.

Two families of shorter canonical aliases resolve to the same content: agentic-os://presets/{role} for a role preset and agentic-os://qe/blueprints/{stage}/{id} for a QE blueprint. list_presets and list_qe_blueprints return these aliases as each item's uri, and search_methodology returns the blueprint alias for a blueprint hit — it searches markdown documents only, so a preset (JSON) can never be a search_methodology result in the first place. Both alias forms, and the plain file/ form, always resolve via get_document or a direct resource read.

Prompts

Ready-made entry points your assistant can offer by name:

PromptWhat it does
agentic-initInterview to set up the governance guardrails in your repo
agentic-doctorVerify an existing agentic-os install is healthy
agentic-upgradeUpgrade the installed templates to a newer version
sdlc-startRun the governed SDLC pipeline on a task, stopping at human gates
sdlc-taskLightweight SDLC flow for a small (XS/S/M) task
qe-blueprint-scaffoldScaffold a fill-in-ready QE agent framework from a blueprint

Changelog

See CHANGELOG.md.

Releasing

Maintainer-only. See RELEASE.md for the one-time setup and per-release runbook.

Requirements

Node >= 20.

Build & test

npm install
npm run build   # build:content (indexes plugins/ + copies dist/content) + build:ts
npm test        # vitest — requires the build above, since the content layer reads dist/content

Docker

npx -y agentic-os-mcp is the supported install path; the Dockerfile at the repository root exists for MCP directories that introspect a server by building and running it in a sandbox. It is equivalent, not an alternative to recommend.

Build from the repository root, never from mcp/ — the content build reads plugins/**, the root LICENSE/NOTICE, and git ls-files:

docker build -t agentic-os-mcp .

The server speaks JSON-RPC over stdio, so -i is required and -t must not be used (a TTY corrupts the stream):

docker run --rm -i agentic-os-mcp

FAQs

Package last updated on 28 Jul 2026

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