buildkite

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GitHub Repo

Overview

The buildkite MCP server is a Model Context Protocol (MCP) server that allows AI assistants and agents to interact directly with Buildkite CI/CD pipelines through a structured, AI-friendly interface. It enables AI-driven workflows to inspect builds, trigger pipelines, monitor execution status, and analyze failures — all without switching tools or manually navigating the Buildkite UI.

This server is well suited for developer productivity, CI/CD observability, incident response, and automated release workflows powered by AI.

Transport

stdio

Tools

  • get_cluster
  • list_clusters
  • get_cluster_queue
  • list_cluster_queues
  • get_pipeline
  • list_pipelines
  • create_pipeline
  • update_pipeline
  • list_builds
  • get_build
  • create_build
  • get_build_test_engine_runs
  • get_jobs
  • get_job_logs
  • list_artifacts
  • get_artifact
  • list_annotations
  • list_test_runs
  • get_test_run
  • get_failed_executions
  • get_test
  • access_token
  • current_user
  • user_token_organization

Key Capabilities

  • CI/CD visibility — Inspect pipelines, builds, and execution status directly from AI workflows.
  • Build triggering and orchestration — Start new builds with parameters as part of automated or conversational flows.
  • Failure analysis — Retrieve and analyze build logs to help diagnose errors or regressions.
  • Pipeline metadata access — Explore pipeline structure, steps, and configuration for understanding or documentation.
  • Workflow automation — Integrate CI/CD actions into larger AI-driven development or release processes.

How It Works

The buildkite MCP server runs as a local or containerized MCP service and connects to Buildkite using an API access token provided via environment configuration. Once started, it exposes Buildkite operations as MCP tools that AI clients can invoke.

When an agent calls a tool, the server translates the MCP request into a Buildkite REST API call, executes it on behalf of the user, and returns structured results over the MCP protocol. Logs and build metadata are normalized so they can be easily reasoned over or summarized by the AI assistant.

By abstracting away authentication, API pagination, and request formatting, the server makes Buildkite appear as a native capability inside MCP-compatible AI clients. This enables workflows such as “trigger a release build,” “summarize why the last build failed,” or “monitor pipeline health” — all through natural language and AI-driven automation.