CircleCI MCP Server

What is CircleCI MCP Server about?

Enables AI‑driven clients (Cursor, Claude, Windsurf, VS Code, etc.) to communicate with CircleCI through the Model Context Protocol, turning natural‑language requests into CI actions such as listing projects, triggering pipelines, fetching logs, and analyzing test results.

How to use CircleCI MCP Server?

1. Install via npx (preferred) or Docker.

   {
     "mcpServers": {
       "circleci-mcp-server": {
         "command": "npx",
         "args": ["-y", "@circleci/mcp-server-circleci"],
         "env": {
           "CIRCLECI_TOKEN": "<YOUR_CIRCLECI_TOKEN>",
           "CIRCLECI_BASE_URL": "https://circleci.com"
         }
       }
     }
   }
   

2. Add the configuration to the MCP client configuration file (e.g., .vscode/mcp.json, claude_desktop_config.json, mcp.json).
3. Start the client; the server will read the token from the environment and expose MCP endpoints.
4. Issue natural‑language commands like “Find the latest failed pipeline on my branch and get logs” and the server will translate them into CircleCI API calls.

Key features of CircleCI MCP Server

• Project discovery – list_followed_projects returns all CircleCI projects you follow.
• Pipeline control – run_pipeline, run_rollback_pipeline, rerun_workflow allow triggering and managing pipelines.
• Log and test retrieval – get_build_failure_logs, get_job_test_results, find_flaky_tests pull detailed CI information.
• Configuration assistance – config_helper validates .circleci/config.yml.
• Analytics tools – download_usage_api_data and find_underused_resource_classes help with cost‑optimization.
• Prompt utilities – create_prompt_template and recommend_prompt_template_tests support AI‑prompt engineering.
• Diff analysis – analyze_diff checks git diffs against Cursor rules.

Use cases of CircleCI MCP Server

• Debugging builds: A developer asks the IDE to “show me the failure logs for the last pipeline on my feature branch.”
• Automated rollbacks: Operations teams trigger a rollback with a single natural‑language command.
• Cost optimization: Data‑engineers download usage data and identify under‑utilized resource classes.
• Prompt engineering: Product teams generate and test AI prompt templates directly from the codebase.
• Continuous compliance: CI configuration is validated on‑the‑fly before committing.

FAQ

Q: Which environment variable is mandatory? A: CIRCLECI_TOKEN must be provided; it needs read (and optionally write) access to your CircleCI projects.

Q: Can I run the server without Node installed? A: Yes, use the Docker image circleci:mcp-server-circleci and pass the same environment variables.

Q: How do I configure a remote MCP server? A: Start the Docker container with -e start=remote -e port=8000 (or any port) and point the client’s url field to http://<host>:8000/mcp.

Q: Do I need to set CIRCLECI_BASE_URL? A: Only for on‑prem CircleCI installations; otherwise the default https://circleci.com is used.

Q: Is there a TypeScript definition for the MCP methods? A: The package ships with type definitions that can be imported from @circleci/mcp-server-circleci.

CircleCI MCP Server

Model Context Protocol (MCP) is a new, standardized protocol for managing context between large language models (LLMs) and external systems. In this repository, we provide an MCP Server for CircleCI.

This lets you use Cursor IDE, Windsurf, Copilot, or any MCP supported Client, to use natural language to accomplish things with CircleCI, e.g.:

• Find the latest failed pipeline on my branch and get logs https://github.com/CircleCI-Public/mcp-server-circleci/wiki#circleci-mcp-server-with-cursor-ide

https://github.com/user-attachments/assets/3c765985-8827-442a-a8dc-5069e01edb74

Requirements

• CircleCI Personal API Token - you can generate one through the CircleCI. Learn more or click here for quick access.

For NPX installation:

• pnpm package manager - Learn more
• Node.js >= v18.0.0

For Docker installation:

• Docker - Learn more

Installation

Cursor

Using NPX in a local MCP Server

Add the following to your cursor MCP config:

{
  "mcpServers": {
    "circleci-mcp-server": {
      "command": "npx",
      "args": ["-y", "@circleci/mcp-server-circleci"],
      "env": {
        "CIRCLECI_TOKEN": "your-circleci-token",
        "CIRCLECI_BASE_URL": "https://circleci.com" // Optional - required for on-prem customers only
      }
    }
  }
}

Using Docker in a local MCP Server

Add the following to your cursor MCP config:

{
  "mcpServers": {
    "circleci-mcp-server": {
      "command": "docker",
      "args": [
        "run",
        "--rm",
        "-i",
        "-e",
        "CIRCLECI_TOKEN",
        "-e",
        "CIRCLECI_BASE_URL",
        "circleci:mcp-server-circleci"
      ],
      "env": {
        "CIRCLECI_TOKEN": "your-circleci-token",
        "CIRCLECI_BASE_URL": "https://circleci.com" // Optional - required for on-prem customers only
      }
    }
  }
}

Using a Self-Managed Remote MCP Server

Add the following to your cursor MCP config:

{
  "inputs": [
    {
      "type": "promptString",
      "id": "circleci-token", 
      "description": "CircleCI API Token",
      "password": true
    }
  ],
  "servers": {
    "circleci-mcp-server-remote": {
      "url": "http://your-circleci-remote-mcp-server-endpoint:8000/mcp"
    }
  }
}

VS Code

Using NPX in a local MCP Server

To install CircleCI MCP Server for VS Code in .vscode/mcp.json:

{
  // 💡 Inputs are prompted on first server start, then stored securely by VS Code.
  "inputs": [
    {
      "type": "promptString",
      "id": "circleci-token",
      "description": "CircleCI API Token",
      "password": true
    },
    {
      "type": "promptString",
      "id": "circleci-base-url",
      "description": "CircleCI Base URL",
      "default": "https://circleci.com"
    }
  ],
  "servers": {
    // https://github.com/ppl-ai/modelcontextprotocol/
    "circleci-mcp-server": {
      "type": "stdio",
      "command": "npx",
      "args": ["-y", "@circleci/mcp-server-circleci"],
      "env": {
        "CIRCLECI_TOKEN": "${input:circleci-token}",
        "CIRCLECI_BASE_URL": "${input:circleci-base-url}"
      }
    }
  }
}

Using Docker in a local MCP Server

To install CircleCI MCP Server for VS Code in .vscode/mcp.json using Docker:

{
  // 💡 Inputs are prompted on first server start, then stored securely by VS Code.
  "inputs": [
    {
      "type": "promptString",
      "id": "circleci-token",
      "description": "CircleCI API Token",
      "password": true
    },
    {
      "type": "promptString",
      "id": "circleci-base-url",
      "description": "CircleCI Base URL",
      "default": "https://circleci.com"
    }
  ],
  "servers": {
    // https://github.com/ppl-ai/modelcontextprotocol/
    "circleci-mcp-server": {
      "type": "stdio",
      "command": "docker",
      "args": [
        "run",
        "--rm",
        "-i",
        "-e",
        "CIRCLECI_TOKEN",
        "-e",
        "CIRCLECI_BASE_URL",
        "circleci:mcp-server-circleci"
      ],
      "env": {
        "CIRCLECI_TOKEN": "${input:circleci-token}",
        "CIRCLECI_BASE_URL": "${input:circleci-base-url}"
      }
    }
  }
}

Using a Self-Managed Remote MCP Server

To install CircleCI MCP Server for VS Code in .vscode/mcp.json using a self-managed remote MCP server:

{
  "servers": {
    "circleci-mcp-server-remote": {
      "type": "sse",
      "url": "http://your-circleci-remote-mcp-server-endpoint:8000/mcp"
    }
  }
}

Claude Desktop

Using NPX in a local MCP Server

Add the following to your claude_desktop_config.json:

{
  "mcpServers": {
    "circleci-mcp-server": {
      "command": "npx",
      "args": ["-y", "@circleci/mcp-server-circleci"],
      "env": {
        "CIRCLECI_TOKEN": "your-circleci-token",
        "CIRCLECI_BASE_URL": "https://circleci.com" // Optional - required for on-prem customers only
      }
    }
  }
}

To locate this file:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

Windows: %APPDATA%\Claude\claude_desktop_config.json

Claude Desktop setup

Using Docker in a local MCP Server

Add the following to your claude_desktop_config.json:

{
  "mcpServers": {
    "circleci-mcp-server": {
      "command": "docker",
      "args": [
        "run",
        "--rm",
        "-i",
        "-e",
        "CIRCLECI_TOKEN",
        "-e",
        "CIRCLECI_BASE_URL",
        "circleci:mcp-server-circleci"
      ],
      "env": {
        "CIRCLECI_TOKEN": "your-circleci-token",
        "CIRCLECI_BASE_URL": "https://circleci.com" // Optional - required for on-prem customers only
      }
    }
  }
}

To find/create this file, first open your claude desktop settings. Then click on "Developer" in the left-hand bar of the Settings pane, and then click on "Edit Config"

This will create a configuration file at:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json

See the guide below for more information on using MCP servers with Claude Desktop: https://modelcontextprotocol.io/quickstart/user

Using a Self-Managed Remote MCP Server

Create a wrapper script first

Create a script file such as 'circleci-remote-mcp.sh':

#!/bin/bash
export CIRCLECI_TOKEN="your-circleci-token"
npx mcp-remote http://your-circleci-remote-mcp-server-endpoint:8000/mcp --allow-http 

Make it executable:

chmod +x circleci-remote-mcp.sh

Then add the following to your claude_desktop_config.json:

{
  "mcpServers": {
    "circleci-remote-mcp-server": {
      "command": "/full/path/to/circleci-remote-mcp.sh"
    }
  }
}

To find/create this file, first open your Claude Desktop settings. Then click on "Developer" in the left-hand bar of the Settings pane, and then click on "Edit Config"

This will create a configuration file at:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json

See the guide below for more information on using MCP servers with Claude Desktop: https://modelcontextprotocol.io/quickstart/user

Claude Code

Using NPX in a local MCP Server

After installing Claude Code, run the following command:

claude mcp add circleci-mcp-server -e CIRCLECI_TOKEN=your-circleci-token -- npx -y @circleci/mcp-server-circleci

Using Docker in a local MCP Server

After installing Claude Code, run the following command:

claude mcp add circleci-mcp-server -e CIRCLECI_TOKEN=your-circleci-token -e CIRCLECI_BASE_URL=https://circleci.com -- docker run --rm -i -e CIRCLECI_TOKEN -e CIRCLECI_BASE_URL circleci:mcp-server-circleci

See the guide below for more information on using MCP servers with Claude Code: https://docs.anthropic.com/en/docs/agents-and-tools/claude-code/tutorials#set-up-model-context-protocol-mcp

Using Self-Managed Remote MCP Server

After installing Claude Code, run the following command:

claude mcp add circleci-mcp-server -e CIRCLECI_TOKEN=your-circleci-token -- npx mcp-remote http://your-circleci-remote-mcp-server-endpoint:8000/mcp --allow-http

See the guide below for more information on using MCP servers with Claude Code: https://docs.anthropic.com/en/docs/agents-and-tools/claude-code/tutorials#set-up-model-context-protocol-mcp

Windsurf

Using NPX in a local MCP Server

Add the following to your windsurf mcp_config.json:

{
  "mcpServers": {
    "circleci-mcp-server": {
      "command": "npx",
      "args": ["-y", "@circleci/mcp-server-circleci"],
      "env": {
        "CIRCLECI_TOKEN": "your-circleci-token",
        "CIRCLECI_BASE_URL": "https://circleci.com" // Optional - required for on-prem customers only
      }
    }
  }
}

Using Docker in a local MCP Server

Add the following to your windsurf mcp_config.json:

{
  "mcpServers": {
    "circleci-mcp-server": {
      "command": "docker",
      "args": [
        "run",
        "--rm",
        "-i",
        "-e",
        "CIRCLECI_TOKEN",
        "-e",
        "CIRCLECI_BASE_URL",
        "circleci:mcp-server-circleci"
      ],
      "env": {
        "CIRCLECI_TOKEN": "your-circleci-token",
        "CIRCLECI_BASE_URL": "https://circleci.com" // Optional - required for on-prem customers only
      }
    }
  }
}

Using Self-Managed Remote MCP Server

Add the following to your windsurf mcp_config.json:

{
  "mcpServers": {
    "circleci": {
      "command": "npx",
      "args": [
        "mcp-remote",
        "http://your-circleci-remote-mcp-server-endpoint:8000/mcp",
        "--allow-http"
      ],
      "disabled": false,
      "alwaysAllow": []
    }
  }
}

See the guide below for more information on using MCP servers with windsurf: https://docs.windsurf.com/windsurf/mcp

Installing via Smithery

To install CircleCI MCP Server for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install @CircleCI-Public/mcp-server-circleci --client claude

Amazon Q Developer CLi

MCP client configuration in Amazon Q Developer is stored in JSON format, in a file named mcp.json.

Amazon Q Developer CLI supports two levels of MCP configuration:

Global Configuration: ~/.aws/amazonq/mcp.json - Applies to all workspaces

Workspace Configuration: .amazonq/mcp.json - Specific to the current workspace

Both files are optional; neither, one, or both can exist. If both files exist, Amazon Q Developer reads MCP configuration from both and combines them, taking the union of their contents. If there is a conflict (i.e., a server defined in the global config is also present in the workspace config), a warning is displayed and only the server entry in the workspace config is used.

Using NPX in a local MCP Server

Edit your global configuration file ~/.aws/amazonq/mcp.json or create a new one in the current workspace .amazonq/mcp.json with the following content:

{
  "mcpServers": {
    "circleci-local": {
      "command": "npx",
      "args": [
        "-y",
        "@circleci/mcp-server-circleci"
      ],
      "env": {
        "CIRCLECI_TOKEN": "YOUR_CIRCLECI_TOKEN",
        "CIRCLECI_BASE_URL": "https://circleci.com" // Optional - required for on-prem customers only
      },
      "timeout": 60000
    }
  }
}

Using a Self-Managed Remote MCP Server

Create a wrapper script first

Create a script file such as 'circleci-remote-mcp.sh':

#!/bin/bash
export CIRCLECI_TOKEN="your-circleci-token"
npx mcp-remote http://your-circleci-remote-mcp-server-endpoint:8000/mcp --allow-http

Make it executable:

chmod +x circleci-remote-mcp.sh

Then add it:

q mcp add --name circleci --command "/full/path/to/circleci-remote-mcp.sh"

Amazon Q Developer in the IDE

Using NPX in a local MCP Server

Edit your global configuration file ~/.aws/amazonq/mcp.json or create a new one in the current workspace .amazonq/mcp.json with the following content:

{
  "mcpServers": {
    "circleci-local": {
      "command": "npx",
      "args": [
        "-y",
        "@circleci/mcp-server-circleci"
      ],
      "env": {
        "CIRCLECI_TOKEN": "YOUR_CIRCLECI_TOKEN",
        "CIRCLECI_BASE_URL": "https://circleci.com" // Optional - required for on-prem customers only
      },
      "timeout": 60000
    }
  }
}

Using a Self-Managed Remote MCP Server

Create a wrapper script first

Create a script file such as 'circleci-remote-mcp.sh':

#!/bin/bash
npx mcp-remote http://your-circleci-remote-mcp-server-endpoint:8000/mcp --allow-http

Make it executable:

chmod +x circleci-remote-mcp.sh

Then add it to the Q Developer in your IDE:

Access the MCP configuration UI (https://docs.aws.amazon.com/amazonq/latest/qdeveloper-ug/mcp-ide.html#mcp-ide-configuration-access-ui).

Choose the plus (+) symbol.

Select the scope: global or local.

If you select global scope, the MCP server configuration is stored in ~/.aws/amazonq/mcp.json and available across all your projects. If you select local scope, the configuration is stored in .amazonq/mcp.json within your current project.

In the Name field, enter the name of the CircleCI remote MCP server (e.g. circleci-remote-mcp).

Select the transport protocol (stdio).

In the Command field, enter the shell command created previously that the MCP server will run when it initializes (e.g. /full/path/to/circleci-remote-mcp.sh).

Click the Save button.

Features

Supported Tools

• get_build_failure_logs

  Retrieves detailed failure logs from CircleCI builds. This tool can be used in three ways:

  1. Using Project Slug and Branch (Recommended Workflow):

     • First, list your available projects:
       • Use the list_followed_projects tool to get your projects
       • Example: "List my CircleCI projects"
       • Then choose the project, which has a projectSlug associated with it
       • Example: "Lets use my-project"
     • Then ask to retrieve the build failure logs for a specific branch:
       • Example: "Get build failures for my-project on the main branch"

  2. Using CircleCI URLs:

     • Provide a failed job URL or pipeline URL directly
     • Example: "Get logs from https://app.circleci.com/pipelines/github/org/repo/123"

  3. Using Local Project Context:

     • Works from your local workspace by providing:
       • Workspace root path
       • Git remote URL
       • Branch name
     • Example: "Find the latest failed pipeline on my current branch"

  The tool returns formatted logs including:

  • Job names
  • Step-by-step execution details
  • Failure messages and context

  This is particularly useful for:

  • Debugging failed builds
  • Analyzing test failures
  • Investigating deployment issues
  • Quick access to build logs without leaving your IDE

• find_flaky_tests

  Identifies flaky tests in your CircleCI project by analyzing test execution history. This leverages the flaky test detection feature described here: https://circleci.com/blog/introducing-test-insights-with-flaky-test-detection/#flaky-test-detection

  This tool can be used in three ways:

  1. Using Project Slug (Recommended Workflow):

     • First, list your available projects:
       • Use the list_followed_projects tool to get your projects
       • Example: "List my CircleCI projects"
       • Then choose the project, which has a projectSlug associated with it
       • Example: "Lets use my-project"
     • Then ask to retrieve the flaky tests:
       • Example: "Get flaky tests for my-project"

  2. Using CircleCI Project URL:

     • Provide the project URL directly from CircleCI
     • Example: "Find flaky tests in https://app.circleci.com/pipelines/github/org/repo"

  3. Using Local Project Context:

     • Works from your local workspace by providing:
       • Workspace root path
       • Git remote URL
     • Example: "Find flaky tests in my current project"

  The tool can be used in two ways:

  1. Using text output mode (default):
     • This will return the flaky tests and their details in a text format
  2. Using file output mode: (requires the FILE_OUTPUT_DIRECTORY environment variable to be set)
     • This will create a directory with the flaky tests and their details

  The tool returns detailed information about flaky tests, including:

  • Test names and file locations
  • Failure messages and contexts

  This helps you:

  • Identify unreliable tests in your test suite
  • Get detailed context about test failures
  • Make data-driven decisions about test improvements

• get_latest_pipeline_status

  Retrieves the status of the latest pipeline for a given branch. This tool can be used in three ways:

  1. Using Project Slug and Branch (Recommended Workflow):

     • First, list your available projects:
       • Use the list_followed_projects tool to get your projects
       • Example: "List my CircleCI projects"
       • Then choose the project, which has a projectSlug associated with it
       • Example: "Lets use my-project"
     • Then ask to retrieve the latest pipeline status for a specific branch:
       • Example: "Get the status of the latest pipeline for my-project on the main branch"

  2. Using CircleCI Project URL:

     • Provide the project URL directly from CircleCI
     • Example: "Get the status of the latest pipeline for https://app.circleci.com/pipelines/github/org/repo"

  3. Using Local Project Context:

     • Works from your local workspace by providing:
       • Workspace root path
       • Git remote URL
       • Branch name
     • Example: "Get the status of the latest pipeline for my current project"

  The tool returns a formatted status of the latest pipeline:

  • Workflow names and their current status
  • Duration of each workflow
  • Creation and completion timestamps
  • Overall pipeline health

  Example output:

  ---
  Workflow: build
  Status: success
  Duration: 5 minutes
  Created: 4/20/2025, 10:15:30 AM
  Stopped: 4/20/2025, 10:20:45 AM
  ---
  Workflow: test
  Status: running
  Duration: unknown
  Created: 4/20/2025, 10:21:00 AM
  Stopped: in progress
  

  This is particularly useful for:

  • Checking the status of the latest pipeline
  • Getting the status of the latest pipeline for a specific branch
  • Quickly checking the status of the latest pipeline without leaving your IDE

• get_job_test_results

  Retrieves test metadata for CircleCI jobs, allowing you to analyze test results without leaving your IDE. This tool can be used in three ways:

  1. Using Project Slug and Branch (Recommended Workflow):

     • First, list your available projects:
       • Use the list_followed_projects tool to get your projects
       • Example: "List my CircleCI projects"
       • Then choose the project, which has a projectSlug associated with it
       • Example: "Lets use my-project"
     • Then ask to retrieve the test results for a specific branch:
       • Example: "Get test results for my-project on the main branch"

  2. Using CircleCI URL:

     • Provide a CircleCI URL in any of these formats:
       • Job URL: "https://app.circleci.com/pipelines/github/org/repo/123/workflows/abc-def/jobs/789"
       • Workflow URL: "https://app.circleci.com/pipelines/github/org/repo/123/workflows/abc-def"
       • Pipeline URL: "https://app.circleci.com/pipelines/github/org/repo/123"
     • Example: "Get test results for https://app.circleci.com/pipelines/github/org/repo/123/workflows/abc-def"

  3. Using Local Project Context:

     • Works from your local workspace by providing:
       • Workspace root path
       • Git remote URL
       • Branch name
     • Example: "Get test results for my current project on the main branch"

  The tool returns detailed test result information:

  • Summary of all tests (total, successful, failed)
  • Detailed information about failed tests including:
    • Test name and class
    • File location
    • Error messages
    • Runtime duration
  • List of successful tests with timing information
  • Filter by tests result

  This is particularly useful for:

  • Quickly analyzing test failures without visiting the CircleCI web UI
  • Identifying patterns in test failures
  • Finding slow tests that might need optimization
  • Checking test coverage across your project
  • Troubleshooting flaky tests

  Note: The tool requires that test metadata is properly configured in your CircleCI config. For more information on setting up test metadata collection, see: https://circleci.com/docs/collect-test-data/

• config_helper

  Assists with CircleCI configuration tasks by providing guidance and validation. This tool helps you:

  1. Validate CircleCI Config:
     • Checks your .circleci/config.yml for syntax and semantic errors
     • Example: "Validate my CircleCI config"

  The tool provides:

  • Detailed validation results
  • Configuration recommendations

  This helps you:

  • Catch configuration errors before pushing
  • Learn CircleCI configuration best practices
  • Troubleshoot configuration issues
  • Implement CircleCI features correctly

• create_prompt_template

  Helps generate structured prompt templates for AI-enabled applications based on feature requirements. This tool:

  1. Converts Feature Requirements to Structured Prompts:
     • Transforms user requirements into optimized prompt templates
     • Example: "Create a prompt template for generating bedtime stories by age and topic"

  The tool provides:

  • A structured prompt template
  • A context schema defining required input parameters

  This helps you:

  • Create effective prompts for AI applications
  • Standardize input parameters for consistent results
  • Build robust AI-powered features

• recommend_prompt_template_tests

  Generates test cases for prompt templates to ensure they produce expected results. This tool:

  1. Provides Test Cases for Prompt Templates:
     • Creates diverse test scenarios based on your prompt template and context schema
     • Example: "Generate tests for my bedtime story prompt template"

  The tool provides:

  • An array of recommended test cases
  • Various parameter combinations to test template robustness

  This helps you:

  • Validate prompt template functionality
  • Ensure consistent AI responses across inputs
  • Identify edge cases and potential issues
  • Improve overall AI application quality

• list_followed_projects

  Lists all projects that the user is following on CircleCI. This tool:

  1. Retrieves and Displays Projects:
     • Shows all projects the user has access to and is following
     • Provides the project name and projectSlug for each entry
     • Example: "List my CircleCI projects"

  The tool returns a formatted list of projects, example output:

  Projects followed:
  1. my-project (projectSlug: gh/organization/my-project)
  2. another-project (projectSlug: gh/organization/another-project)
  

  This is particularly useful for:

  • Identifying which CircleCI projects are available to you
  • Obtaining the projectSlug needed for other CircleCI tools
  • Selecting a project for subsequent operations

  Note: The projectSlug (not the project name) is required for many other CircleCI tools, and will be used for those tool calls after a project is selected.

• run_pipeline

  Triggers a pipeline to run. This tool can be used in three ways:

  1. Using Project Slug and Branch (Recommended Workflow):

     • First, list your available projects:
       • Use the list_followed_projects tool to get your projects
       • Example: "List my CircleCI projects"
       • Then choose the project, which has a projectSlug associated with it
       • Example: "Lets use my-project"
     • Then ask to run the pipeline for a specific branch:
       • Example: "Run the pipeline for my-project on the main branch"

  2. Using CircleCI URL:

     • Provide a CircleCI URL in any of these formats:
       • Job URL: "https://app.circleci.com/pipelines/github/org/repo/123/workflows/abc-def/jobs/789"
       • Workflow URL: "https://app.circleci.com/pipelines/github/org/repo/123/workflows/abc-def"
       • Pipeline URL: "https://app.circleci.com/pipelines/github/org/repo/123"
       • Project URL with branch: "https://app.circleci.com/projects/github/org/repo?branch=main"
     • Example: "Run the pipeline for https://app.circleci.com/pipelines/github/org/repo/123/workflows/abc-def"

  3. Using Local Project Context:

     • Works from your local workspace by providing:
       • Workspace root path
       • Git remote URL
       • Branch name
     • Example: "Run the pipeline for my current project on the main branch"

  The tool returns a link to monitor the pipeline execution.

  This is particularly useful for:

  • Quickly running pipelines without visiting the CircleCI web UI
  • Running pipelines from a specific branch

• run_rollback_pipeline

  This tool allows for triggering a rollback for a project. It requires the following parameters;

  • project_id - The ID of the CircleCI project (UUID)
  • environmentName - The environment name
  • componentName - The component name
  • currentVersion - The current version
  • targetVersion - The target version
  • namespace - The namespace of the component
  • reason - The reason for the rollback (optional)
  • parameters - The extra parameters for the rollback pipeline (optional)

  If not all the parameters are provided right away, the toll will make use of other tools to try and retrieve all the required info. The rollback can be performed in two different way, depending on whether a rollback pipeline definition has been configured for the project:

  • Pipeline Rollback: will trigger the rollback pipeline.
  • Workflow Rerun: will trigger the rerun of a previous workflow.

  A typical interaction with this tool will follow this pattern:

  1. Project Selection - Retrieve list of followed projects and prompt user to select one
  2. Environment Selection - List available environments and select target (auto-select if only one exists)
  3. Component Selection - List available components and select target (auto-select if only one exists)
  4. Version Selection - Display available versions, user selects non-live version for rollback
  5. Rollback Mode Detection - Check if rollback pipeline is configured for the selected project
  6. Execute Rollback - Two options available:
  • Pipeline Rollback: Prompt for optional reason, execute rollback pipeline
  • Workflow Rerun**: Rerun workflow using selected version's workflow ID
  7. Confirmation - Summarize rollback request and confirm before execution

• rerun_workflow

  Reruns a workflow from its start or from the failed job.

  The tool returns the ID of the newly-created workflow, and a link to monitor the new workflow.

  This is particularly useful for:

  • Quickly rerunning a workflow from its start or from the failed job without visiting the CircleCI web UI

• analyze_diff

  Analyzes git diffs against cursor rules to identify rule violations.

  This tool can be used by providing:

  1. Git Diff Content:

     • Staged changes: git diff --cached
     • Unstaged changes: git diff
     • All changes: git diff HEAD
     • Example: "Analyze my staged changes against the cursor rules"

  2. Repository Rules:

     • Rules from .cursorrules file in your repository root
     • Rules from .cursor/rules directory
     • Multiple rule files combined with --- separator
     • Example: "Check my diff against the TypeScript coding standards"

  The tool provides:

  • Detailed violation reports with confidence scores
  • Specific explanations for each rule violation

  Example usage scenarios:

  • "Analyze my staged changes for any rule violations"
  • "Check my unstaged changes against rules"

  This is particularly useful for:

  • Pre-commit code quality checks
  • Ensuring consistency with team coding standards
  • Catching rule violations before code review

  The tool integrates with your existing cursor rules setup and provides immediate feedback on code quality, helping you catch issues early in the development process.

• list_component_versions

  Lists all versions for a specific CircleCI component in an environment. This tool retrieves version history including deployment status, commit information, and timestamps for a component. The tool will prompt the user to select the component and environment from a list if not provided.

  Example output:

  Versions for the component: {
    "items": [
      {
        "name": "v1.2.0",
        "namespace": "production",
        "environment_id": "env-456def",
        "is_live": true,
        "pipeline_id": "12345678-1234-1234-1234-123456789abc",
        "workflow_id": "87654321-4321-4321-4321-cba987654321",
        "job_id": "11111111-1111-1111-1111-111111111111",
        "job_number": 42,
        "last_deployed_at": "2023-01-01T00:00:00Z"
      },
      {
        "name": "v1.1.0",
        "namespace": "production", 
        "environment_id": "env-456def",
        "is_live": false,
        "pipeline_id": "22222222-2222-2222-2222-222222222222",
        "workflow_id": "33333333-3333-3333-3333-333333333333",
        "job_id": "44444444-4444-4444-4444-444444444444",
        "job_number": 38,
        "last_deployed_at": "2023-01-03T00:00:00Z"
      }
    ]
  }
  

  This is useful for:

  • Identifying which versions were deployed for a component
  • Finding the currently live version in an environment
  • Selecting target versions for rollback operations
  • Getting deployment details like pipeline, workflow, and job information
  • Listing all environments
  • Listing all components

• download_usage_api_data

  Downloads usage data from the CircleCI Usage API for a given organization. Accepts flexible, natural language date input (e.g., "March 2025" or "last month"). Cloud-only feature.

  This tool can be used in one of two ways:

  1. Start a new export job for a date range (max 32 days) by providing:
  • orgId: Organization ID
  • startDate: Start date (YYYY-MM-DD or natural language)
  • endDate: End date (YYYY-MM-DD or natural language)
  • outputDir: Directory to save the CSV file
  2. Check/download an existing export job by providing:
  • orgId: Organization ID
  • jobId: Usage export job ID
  • outputDir: Directory to save the CSV file

  The tool provides:

  • A csv containing the CircleCI Usage API data from the specified time frame

  This is useful for:

  • Downloading detailed CircleCI usage data for reporting or analysis
  • Feeding usage data into the find_underused_resource_classes tool

  Example usage scenarios:

• Scenario 1:

  1. "Download usage data for org abc123 from June into ~/Downloads"
  2. "Check status"

• Scenario 2:

  1. "Download usage data for org abc123 for last month to my Downloads folder"
  2. "Check usage download status"
  3. "Check status again"

• Scenario 3:

  1. "Check my usage export job usage-job-9f2d7c and download it if ready"

• find_underused_resource_classes

  Analyzes a CircleCI usage data CSV file to find jobs/resource classes with average or max CPU/RAM usage below a given threshold (default 40%).

  This tool can be used by providing:

  • A csv containing CircleCI Usage API data, which can be obtained by using the download_usage_api_data tool.

  The tool provides:

  • A markdown list of all jobs that are below the threshold, delineated by project and workflow.

  This is useful for:

  • Finding jobs that are using less than half of the compute provided to them on average
  • Generating a list of low hanging cost optimizations

  Example usage scenarios:

  • Scenario 1:
    1. "Find underused resource classes in the file you just downloaded"
  • Scenario 2:
    1. "Find underused resource classes in ~/Downloads/usage-data-2025-06-01_2025-06-30.csv"
  • Scenario 3:
    1. "Analyze /Users/you/Projects/acme/usage-data-job-9f2d7c.csv with threshold 30"

Development

Getting Started

1. Clone the repository:

   git clone https://github.com/CircleCI-Public/mcp-server-circleci.git
   cd mcp-server-circleci
   

2. Install dependencies:

   pnpm install
   

3. Build the project:

   pnpm build
   

Building Docker Container

You can build the Docker container locally using:

docker build -t circleci:mcp-server-circleci .

This will create a Docker image tagged as circleci:mcp-server-circleci that you can use with any MCP client.

To run the container locally:

docker run --rm -i -e CIRCLECI_TOKEN=your-circleci-token -e CIRCLECI_BASE_URL=https://circleci.com circleci:mcp-server-circleci

To run the container as a self-managed remote MCP server you need to add the environment variable start=remote to the docker run command. You can also define the port to use with the environment variable port=<port> or else the default port 8000 will be used:

docker run --rm -i -e CIRCLECI_TOKEN=your-circleci-token -e CIRCLECI_BASE_URL=https://circleci.com circleci:mcp-server-circleci -e start=remote -e port=8000

Development with MCP Inspector

The easiest way to iterate on the MCP Server is using the MCP inspector. You can learn more about the MCP inspector at https://modelcontextprotocol.io/docs/tools/inspector

1. Start the development server:

   pnpm watch # Keep this running in one terminal
   

2. In a separate terminal, launch the inspector:

   pnpm inspector
   

3. Configure the environment:

   • Add your CIRCLECI_TOKEN to the Environment Variables section in the inspector UI
   • The token needs read access to your CircleCI projects
   • Optionally you can set your CircleCI Base URL. Defaults to https//circleci.com

Testing

• Run the test suite:

  pnpm test
  

• Run tests in watch mode during development:

  pnpm test:watch
  

For more detailed contribution guidelines, see CONTRIBUTING.md