MCP Servers

A directory of curated & open-source Model Context Protocol servers. Search and discover MCP servers to enhance your AI capabilities.

Documentation

Manage and search documents intelligently with MCP Documentation Server. Features semantic search, file uploads, and local storage.

Office PowerPoint

An MCP server for PowerPoint automation. Create slides, add charts, apply professional themes, and use your own PPTX templates from an AI model.

MSSql

Connect AI assistants to MSSql databases with reliable .NET-powered MCP server. Features schema discovery, full SQL support, and Docker deployment.

Microsoft Docs

Connect your AI assistant directly to official Microsoft documentation for accurate, real-time answers inside your MCP client.

Zen

Transform Claude into a multi-AI development team with Zen MCP. Get Gemini's deep thinking, O3's logic, and collaborative AI workflows for complex coding tasks.

Typescript

Add LSP-level TypeScript capabilities to AI coding tools with semantic symbol analysis, intelligent refactoring, and type-aware operations.

TeslaMate

Connect TeslaMate PostgreSQL database to AI assistants for natural language Tesla data queries, driving analytics, and battery health monitoring.

Chrome

Let AI control your personal Chrome browser. The Chrome MCP Server extension provides 20+ tools for local and secure browser automation.

Ask Human

A lightweight MCP server that lets AI agents ask for help. Get a full Q&A history in Markdown and prevent debugging headaches.

Claude + Gemini

A Model Context Protocol (MCP) server that gives Claude access to Gemini for collaborative coding, deep analysis, and debugging.

Figma Dev Mode

Transform Figma designs into production code with the official Dev Mode MCP Server. Works with VS Code, Cursor, and Windsurf for AI-powered development workflows.

Facebook Ads Library

Connect Claude to Facebook's public ads library for competitive research, ad analysis, and messaging comparison across any brand or company.

Private Journal

Use the Private Journal MCP Server to provide Claude with a persistent, searchable memory. All processing and data storage is 100% local.

Alpaca

Connect your AI assistants to Alpaca's trading API for natural language stock and options trading with real-time data and portfolio management.

Supermemory

Use the Supermemory MCP server to give your AIs persistent context. A single command installs a universal memory that works across multiple LLM clients.

Open Deep Research

Integrate the Open Deep Research MCP server with your AI assistants. It performs deep web research and provides detailed, reliable reports.

Hugging Face

Official Hugging Face MCP server connecting your MCP clients to Hub APIs for model search, dataset discovery, and research paper exploration.

ClaudePoint

se the ClaudePoint MCP server to create instant codebase checkpoints. Experiment freely with Claude Code and restore your work instantly if something goes wrong.

AntV Chart

Generate 15+ chart types including line, bar, pie, and network graphs through MCP server using AntV visualization library.

Quick Data

Transform any structured dataset into intelligent analytics workflows with this universal MCP server featuring automatic schema detection and AI guidance.

Microsoft Clarity

Use the Microsoft Clarity MCP Server to query web analytics with natural language via AI tools like Claude and Cursor. Easy setup for developers.

FAQs

Q: What exactly is the Model Context Protocol (MCP)?

A: MCP is an open standard, like a common language, that lets AI applications (clients) and external data sources or tools (servers) talk to each other. It helps AI models get the context (data, instructions, tools) they need from outside systems to give more accurate and relevant responses. Think of it as a universal adapter for AI connections.

Q: How is MCP different from OpenAI's function calling or plugins?

A: While OpenAI's tools allow models to use specific external functions, MCP is a broader, open standard. It covers not just tool use, but also providing structured data (Resources) and instruction templates (Prompts) as context. Being an open standard means it's not tied to one company's models or platform. OpenAI has even started adopting MCP in its Agents SDK.

Q: Can I use MCP with frameworks like LangChain?

A: Yes, MCP is designed to complement frameworks like LangChain or LlamaIndex. Instead of relying solely on custom connectors within these frameworks, you can use MCP as a standardized bridge to connect to various tools and data sources. There's potential for interoperability, like converting MCP tools into LangChain tools.

Q: Why was MCP created? What problem does it solve?

A: It was created because large language models often lack real-time information and connecting them to external data/tools required custom, complex integrations for each pair. MCP solves this by providing a standard way to connect, reducing development time, complexity, and cost, and enabling better interoperability between different AI models and tools.

Q: Is MCP secure? What are the main risks?

A: Security is a major consideration. While MCP includes principles like user consent and control, risks exist. These include potential server compromises leading to token theft, indirect prompt injection attacks, excessive permissions, context data leakage, session hijacking, and vulnerabilities in server implementations. Implementing robust security measures like OAuth 2.1, TLS, strict permissions, and monitoring is crucial.

Q: Who is behind MCP?

A: MCP was initially developed and open-sourced by Anthropic. However, it's an open standard with active contributions from the community, including companies like Microsoft and VMware Tanzu who maintain official SDKs.

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