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PEP 810 Proposes Explicit Lazy Imports for Python 3.15
An opt-in lazy import keyword aims to speed up Python startups, especially CLIs, without the ecosystem-wide risks that sank PEP 690.
@octopusdeploy/mcp-server
Advanced tools
The Octopus MCP Server provides your AI assistant with powerful tools that allow it to inspect, query, and diagnose problems within your Octopus instance.
Octopus makes it easy to deliver software to Kubernetes, multi-cloud, on-prem infrastructure, and anywhere else. Automate the release, deployment, and operations of your software and AI workloads with a tool that can handle CD at scale in ways no other tool can.
Model Context Protocol (MCP) allows the AI assistants you use in your day to day work, like Claude Code, or ChatGPT, to connect to the systems and services you own in a standardized fashion, allowing them to pull information from those systems and services to answer questions and perform tasks.
The Octopus MCP Server provides your AI assistant with powerful tools that allow it to inspect, query, and diagnose problems within your Octopus instance, transforming it into your ultimate DevOps wingmate. For a list of supported use-cases and sample prompts, see our documentation.
This project is currently in Early Access, and subject to breaking changes.
Most tools exposed by the MCP Server use stable APIs that have been available from at least version 2021.1
of Octopus Server. Tools that are newer will specify the minimum supported version in the documentation. Alternatively, you can use the command line argument --list-tools-by-version
to check how specific tools relate to versions of Octopus.
Run with environment variables
docker run -i --rm -e OCTOPUS_API_KEY=your-key -e OCTOPUS_SERVER_URL=https://your-octopus.com octopusdeploy/mcp-server
Run with CLI arguments
docker run -i --rm octopusdeploy/mcp-server --server-url https://your-octopus.com --api-key YOUR_API_KEY
Full example configuration (for Claude Desktop, Claude Code, and Cursor):
{
"mcpServers": {
"octopus-deploy": {
"type": "stdio",
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"octopusdeploy/mcp-server",
"--server-url",
"https://your-octopus.com",
"--api-key",
"YOUR_API_KEY"
]
},
}
}
For Apple Mac users, you might need to add the following arguments in the configuration to force Docker to use the Linux platform:
"--platform",
"linux/amd64",
We are planning to release a native ARM build shortly so that those arguments will not be required anymore.
Full example configuration (for Claude Desktop, Claude Code, and Cursor):
{
"mcpServers": {
"octopusdeploy": {
"type": "stdio",
"command": "npx",
"args": ["-y", "@octopusdeploy/mcp-server", "--api-key", "YOUR_API_KEY", "--server-url", "https://your-octopus.com"]
}
}
}
The Octopus MCP Server is typically configured within your AI Client of choice.
It is packaged as an npm package and executed via Node's npx
command. Your configuration will include the command invocation npx
, and a set of arguments that supply the Octopus MCP Server package and provide the Octopus Server URL and API key required, if they are not available as environment variables.
The command line invocation you will be configuring will be one of the two following variants:
npx -y @octopusdeploy/mcp-server
With configuration provided via environment variables:
OCTOPUS_API_KEY=API-KEY
OCTOPUS_SERVER_URL=https://your-octopus.com
Or with configuration supplied via the command line:
npx -y @octopusdeploy/mcp-server --server-url https://your-octopus.com --api-key YOUR_API_KEY
The Octopus MCP Server supports several command-line options to customize which tools are available.
If you are not sure which tools you require, we recommend running without any additional command-line options and using the provided defaults.
Use the --toolsets
parameter to enable specific groups of tools:
# Enable all toolsets (default)
npx -y @octopusdeploy/mcp-server
# Enable only specific toolsets
npx -y @octopusdeploy/mcp-server --toolsets projects,deployments
# Enable all toolsets explicitly
npx -y @octopusdeploy/mcp-server --toolsets all
Available toolsets:
The server runs in read-only mode by default for security. All current tools are read-only operations.
# Run in read-only mode (default)
npx -y @octopusdeploy/mcp-server --read-only
# Disable read-only mode (currently no effect as all tools are read-only)
npx -y @octopusdeploy/mcp-server --read-only=false
# Development setup with only core and project tools
npx -y @octopusdeploy/mcp-server --toolsets core,projects --server-url https://your-octopus.com --api-key YOUR_API_KEY
# Full production setup with all tools
npx -y @octopusdeploy/mcp-server --toolsets all --read-only --server-url https://your-octopus.com --api-key YOUR_API_KEY
--log-level <level>
- Minimum log level (info, error)--log-file <path>
- Log file path or filename. If not specified, logs are written to console only-q, --quiet
- Disable file logging, only log errors to console--list-tools-by-version
- List all registered tools by their supported Octopus Server version and exitlist_spaces
: List all spaces in the Octopus Deploy instancelist_environments
: List all environments in a given spacelist_projects
: List all projects in a given spacelist_deployments
: List deployments in a space with optional filteringget_release_by_id
: Get details for a specific release by its IDlist_releases
: List all releases in a given spacelist_releases_for_project
: List all releases for a specific projectget_task_by_id
: Get details for a specific server task by its IDget_task_details
: Get detailed information for a specific server taskget_task_raw
: Get raw details for a specific server tasklist_tenants
: List all tenants in a given spaceget_tenant_by_id
: Get details for a specific tenant by its IDget_tenant_variables
: Get tenant variables by type (all, common, or project)get_missing_tenant_variables
: Get tenant variables that are missing valuesget_kubernetes_live_status
: Get live status of Kubernetes resources for a project and environment (minimum supported version: 2025.3
)list_deployment_targets
: List all deployment targets in a space with optional filteringget_deployment_target
: Get detailed information about a specific deployment targetlist_certificates
: List all certificates in a space with optional filteringget_certificate
: Get detailed information about a specific certificate by its IDlist_accounts
: List all accounts in a space with optional filteringget_accounts
: Get detailed information about a specific account by its IDget_deployment_process
: Get deployment process by ID for projects or releasesget_branches
: Get Git branches for a version-controlled project (minimum supported version: 2021.2
)get_current_user
: Get information about the current authenticated userWhile the Octopus MCP Server at this stage is a read-only tool, it can read full deployment logs, which could include production secrets. Exercise caution when connecting Octopus MCP to tools and models you do not fully trust.
Running agents in a fully automated fashion could make you vulnerable to exposure via prompt-injection attacks that exfiltrate tokens.
Exercise caution and mitigate the risks by using least-privileged accounts when connecting to Octopus Server.
The nature of current AI chat tools and the MCP protocol itself makes it impractical to analyze large amounts of data. Most MCP clients currently do not support chaining tool calls (using the output of one tool as input to the next one) and instead fall back to copying the results token by token, which frequently leads to hallucinations. If you are looking to process historical data from your Octopus instance for analysis purposes, we recommend using the API directly or writing your own MCP client that is capable of processing the tool call results programmatically.
The MCP Server is technically just a thin layer on top of the existing Octopus Server API. As such it is capable of retrieving large amounts of data (for example, requesting thousands of deployments). Such queries can have a significant effect on your instance's performance. Instruct your models to only retrieve the minimum set of data that it needs (most models are really good at this out of the box).
Contributions are welcome! :heart: Please read our Contributing Guide for information about how to get involved in this project.
We are eager to hear how you plan to use Octopus MCP Server and what features you would like to see included in future version.
Please use Issues to provide feedback, or request features.
If you are a current Octopus customer, please report any issues you experience using our MCP server to our support team. This will ensure you get a timely response within our standard support guarantees.
We are working on integrating an MCP server directly into Octopus Server. This will open up the door for us to build more complex MCP tools, as well as:
If this is of interest to you, please register your interest on our roadmap item.
This project is licensed under the terms of Mozilla Public License 2.0 open source license.
FAQs
The Octopus MCP Server provides your AI assistant with powerful tools that allow it to inspect, query, and diagnose problems within your Octopus instance.
The npm package @octopusdeploy/mcp-server receives a total of 237 weekly downloads. As such, @octopusdeploy/mcp-server popularity was classified as not popular.
We found that @octopusdeploy/mcp-server demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 5 open source maintainers collaborating on the project.
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