MCP Servers

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

Neon Postgres

An MCP Server that lets you use Claude Desktop, or any MCP Client, to use natural language to accomplish things with Neon.

Excel

An MCP server that provides Excel file manipulation capabilities without requiring Microsoft Excel installation.

WhatsApp

An open-source MCP server for WhatsApp that searches your personal Whatsapp messages/contacts, and sends messages to either individuals or groups.

Unreal Engine

An MCP server that allows you to control Unreal Engine through natural language using AI assistant clients like Cursor, Windsurf ,and Claude Desktop.

Markdownify

An Model Context Protocol (MCP) server that converts various file types and web content to Markdown format.

Perplexity

An MCP server implementation that integrates the Perplexity API (Sonar API) to provide Claude with unparalleled real-time, web-wide research.

Azure AI Foundry

An MCP server integrates with Azure AI Foundry to enable connections to your existing Azure AI Agents.

Code Runner

An MCP Server that allows developers to run code snippets and show the result.

Ableton

The Ableton MCP connects Ableton Live to Claude AI through the Model Context Protocol.

Node.js Debugger

An MCP server that gives Cursor or Claude Code access to Node.js at runtime to help you debug.

Cursor Talk to Figma

An MCP server that allows the Cursor to communicate with Figma to read designs and modify them programmatically.

Kubernete

A MCP server for Kubernetes that enables AI assistants like Claude, Cursor, and others to interact with Kubernetes clusters through natural language.

Firecrawl

The official Firecrawl MCP Server that adds powerful web scraping to Cursor, Claude, and any other LLM clients.

Figma Context

An MCP server that gives Cursor, Windsurf, Cline, and other AI-powered coding tools access to your Figma files.

Elasticsearch

A MCP server that connects to your Elasticsearch data directly from any MCP Client (like Claude Desktop).

Blender

An MCP server that connects Blender to Claude AI, and allows Claude to directly interact with and control Blender.

Tripo

An MCP server that allows you to generate 3D assets from natural language using Tripo's API and import to Blender.

Fetcher

An MCP server for fetching web page content using Playwright headless browser.

Zapier

An MCP server that gives your AI assistant direct access to over 7,000+ apps and 30,000+ actions without complex API integrations.

Ghidra

A Model Context Protocol server for allowing LLMs to autonomously reverse engineer applications.

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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