MCP Servers

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

Code Sandbox

Execute Python and JavaScript code safely in AI applications using containerized environments with the Code Sandbox MCP Server.

Apple Docs

Search Apple's official API references, sample code, and WWDC session transcripts with natural language using this MCP server.

Play Store

Deploy Android apps to Google Play Store through Claude using this MCP server. Automate releases, manage rollouts, and promote between tracks via natural language.

n8n

An MCP server that gives AI assistants deep knowledge of n8n nodes for building and validating workflows. Supports npx and Docker.

Image Gen

Use the Image Gen MCP Server to add universal image generation capabilities from OpenAI and Google to any MCP clients like Claude Desktop.

GhidrAssistMCP

Connect AI assistants to Ghidra through GhidrAssistMCP's Model Context Protocol server for automated binary analysis and documentation.

MCP Toolbox for Databases

MCP Toolbox for Databases handles connection pooling and auth, letting you build AI database assistants and tools with just a few lines of code.

VChart

Create professional charts through AI conversations using VChart MCP Server. Supports PNG, HTML, and VChart spec outputs with various chart types.

Screen Monitor

A cross-platform MCP server with 21 tools for AI vision. Let your AI see, understand, and interact with your screen for advanced automation.

Gemini Coding Assistant

Gemini Coding Assistant MCP Server enables Claude Code to consult Google's Gemini AI for complex programming problems with file attachments and session persistence.

Anna’s Archive

A simple MCP server and command-line tool for using the Anna's Archive JSON API to find and download materials.

Nutrient Document Engine

An MCP server to connect AI agents with document processing. Use natural language to extract, edit, and redact documents.

Deep Code Reasoning

Multi-model debugging MCP server with AI-to-AI conversations, execution tracing, and hypothesis testing for distributed system analysis and optimization.

Icons8

Connect your MCP clients to Icons8's 40,000+ icon library. Get SVG and PNG icons instantly through natural language requests in your IDE.

Gemini MCP Tool

A simple MCP server to connect AI assistants like Claude to the Gemini CLI. Analyze large files and codebases using Gemini's context window.

Docfork

An MCP server that provides your AI assistants with the latest documentation for over 9000 libraries to prevent outdated code.

o3 Web Search

Add AI-powered web search to Claude using OpenAI's o3 model through this MCP server. Get intelligent, context-aware search results instantly.

Node.js Debugger

Connect Cursor and Claude Code to live Node.js applications for AI-powered runtime debugging, breakpoint management, and real-time variable inspection.

Debugg AI

Run end-to-end tests with AI agents using the Debugg AI MCP server. Test localhost apps with managed browsers and no complex setup.

Instagram DM

Connect Instagram to Claude Desktop and Cursor with this MCP server. Send messages, manage conversations, and automate Instagram communications.

OpenNutrition

Install the MCP OpenNutrition server for local, private access to comprehensive nutritional data from USDA, CNF, and other sources.

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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