MCP Server

The CI/CD Watch MCP server lets AI assistants query your pipeline data directly. Ask questions like “which pipelines are slowest?” or “what are my DORA metrics?” and get answers grounded in your real CI/CD data.

What is MCP?

The Model Context Protocol (MCP) is an open standard that lets AI assistants connect to external data sources. When you add the CI/CD Watch MCP server, your assistant gains access to tools for querying pipeline runs, metrics, and costs.

Supported Clients

Any MCP-compatible client works, including:

  • Claude Desktop and Claude Code
  • Cursor
  • Windsurf
  • Any client that supports MCP HTTP transport

Setup

1. Create an API key

Go to Settings > API Keys and create a key with readscope. Copy the key — you'll need it in the next step.

2. Add to your MCP client

Add the following to your MCP client configuration. The exact location depends on your client:

  • Claude Desktop: ~/Library/Application Support/Claude/claude_desktop_config.json
  • Claude Code: ~/.claude.json or project .mcp.json
  • Cursor: .cursor/mcp.json in your project
{
  "mcpServers": {
    "cicd-watch": {
      "type": "http",
      "url": "https://mcp.cicd.watch",
      "headers": {
        "Authorization": "Bearer cw_your_key_here"
      }
    }
  }
}

Replace cw_your_key_here with your API key. Nothing to install — the MCP server runs on our infrastructure.

Available Tools

Once connected, your AI assistant can use these tools:

ToolDescription
list-runsList recent pipeline runs, optionally filtered by time period
list-connectionsList all CI/CD provider connections
get-dora-metricsGet DORA metrics (Deployment Frequency, Lead Time, Change Failure Rate, MTTR)
get-costsGet cost breakdown including compute costs, wait time, and waste analysis
get-performanceGet pipeline performance analysis with duration stats, trends, and suggestions

All tools accept an optional periodDaysparameter (1–365) to control the lookback window. Defaults to 30 days for metrics tools.

Example Prompts

Once configured, try asking your AI assistant:

  • >“What are my DORA metrics for the last 30 days?”
  • >“Which pipelines are the most expensive?”
  • >“Show me recent failed builds”
  • >“How is our deployment frequency trending?”
  • >“What optimization opportunities do we have?”