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
Confident AI's official MCP server, connecting AI tools to the platform behind DeepEval for evaluating, observing, and iterating on LLM quality. Agents can trigger and read evaluation runs, inspect metric scores and failing test cases, manage golden datasets, and review production traces to close the loop between eval results and code changes.
Comet's official MCP server for Opik, an open-source LLM evaluation and observability platform. Connects an AI host directly to an Opik workspace so agents can read traces and spans, log evaluation scores, manage prompt versions in the prompt library, and inspect project metrics without leaving the editor.
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.