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
92
Tools
343
Categories
11
Contributors
79
Official GitLab MCP server connecting AI tools to GitLab's DevOps platform. Enables agents to manage projects, issues, merge requests, branches, files, CI/CD pipelines, and the GitLab Duo workflow. Supports both GitLab.com SaaS and self-managed instances with fine-grained access tokens.
MCP server for Atlassian Bitbucket that connects AI tools to repositories, pull requests, branches, and pipelines. Enables agents to review and create pull requests, read file contents and diffs, leave comments, and inspect build status on Bitbucket Cloud and Server.
Skills vs MCP servers
what's the difference?Skillsthe “what to do”
A skillA reusable, structured prompt/workflow with recommended models, an example prompt, and compatible tools. packages know-how — instructions, an example promptA ready-to-use prompt template that demonstrates how to invoke the skill., and recommended models — so an agent performs a task consistently. Skills add knowledge, not new connections.
MCP serversthe “how to connect”
An MCP serverModel Context Protocol server — a standard way to expose tools, resources, and prompts to AI agents and IDEs. gives an agent new capabilities by connecting it to real systems (databases, APIs, files) over a transportHow the client talks to the server: stdio (local process), SSE, or HTTP streaming.. 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.