MCP Servers
The open registry for Model Context Protocol servers. Find the right tools, resources, and prompts for your AI agents — filtered by category, transport, or use case.
Servers
206
Tools
545
Categories
11
Contributors
182
MCP server for Sentry error tracking integration. Enables AI agents to retrieve and analyze issues, view error stack traces, search events by query, and access project performance data. Helps developers debug production errors by providing contextual error information directly in AI-powered development workflows.
Official Netlify MCP server for managing web deployments. Lets AI agents create and configure sites, trigger and monitor deploys, manage environment variables, and read build logs. Useful for shipping frontends and serverless functions, debugging failed builds, and automating deployment workflows from an AI assistant.
MCP server for Jenkins CI/CD. Enables AI agents to trigger builds, inspect job and build status, stream console logs, and diagnose failing pipelines, bringing continuous integration workflows into AI-powered development environments.
Microsoft's official MCP server for Azure DevOps. Brings Azure DevOps capabilities to AI agents, including managing work items and boards, browsing Git repositories and pull requests, querying builds and pipelines, and accessing wikis and test plans. Lets teams drive their Azure DevOps workflows through natural language from MCP-compatible tools.
Buildkite's official MCP server for querying and operating CI/CD pipelines, builds, jobs, artifacts, and organization-level delivery workflows.
Dynatrace's official MCP server that brings the Dynatrace observability platform into AI workflows. Lets assistants query problems and vulnerabilities, run DQL against logs, metrics, and traces, inspect entities, and pull real-time monitoring data directly into a developer's coding environment for faster troubleshooting and root-cause analysis.
Harness' official MCP server for connecting agents to software delivery workflows, including deployment pipelines, services, environments, and delivery insights.
JFrog's MCP server lets agents work with Platform services for artifact repositories, build information, release lifecycle management, and software supply-chain workflows.
Skills vs MCP servers
what’s the difference?Skillsthe “what to do”
A skill packages know-how — instructions, an example prompt, and recommended models — so an agent performs a task consistently. Skills add knowledge, not new connections.
MCP serversthe “how to connect”
An MCP server gives an agent new capabilities by connecting it to real systems (databases, APIs, files) over a transport. MCP adds connections and actions, not task instructions.
Rule of thumb: reach for a skill when you need the model to do a task well, and an MCP server when you need it to reach a tool or system. They compose — a skill can rely on tools an MCP server provides.
Built an MCP server?
Submit it to the registry — it’s open source and community-maintained.