MCP Servers

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

Cursor n8n

Control your n8n workflows directly from Cursor IDE. This MCP server enables AI assistants to create, manage, and debug automations via the n8n API.

Apify

Connect AI assistants to 8000+ web scraping tools via Apify MCP Server. Extract social media data, contact details, and automate web research.

Blueprint

Use the Blueprint MCP Server to generate system architecture diagrams directly from your codebase using Nano Banana Pro.

HOPX

Connect your AI agents to secure, isolated cloud environments with the HOPX MCP Server. Execute Python, JS, and Bash safely without local risks.

Code Execution Mode

Reduce MCP context overhead from 30k to 200 tokens. This bridge enables secure, rootless Python code execution and on-demand tool discovery for Claude.

WPMCP

Use the WordPress MCP Server to give AI assistants full control over your site for content, theme, and plugin management.

MATLAB

Install and configure the MATLAB MCP Server for a direct connection between your local MATLAB session and MCP applications like Claude Desktop.

Claude Skills

An MCP server that brings Anthropic's Claude Skills to AI assistants like Cursor AI, featuring local semantic search and a no-timeout architecture.

Codex MCP

Open-source MCP server for OpenAI Codex CLI integration with Claude and Cursor. Features sandboxed operations, structured changes, and cross-platform compatibility.

ImageSorcery MCP

Use the ImageSorcery MCP server to crop, resize, remove backgrounds, and run object detection on local images with your AI assistant.

Nuxt MCP

Local MCP server implementation for Nuxt.js and Vite projects, providing AI models with full application context during development.

VosDroits

An MCP server that provides search and retrieval for French public service information and tax documents from official government sources.

Skillz

Automatically discovers and registers Anthropic Claude-style skills as MCP tools. Recursive discovery, absolute file paths, and temporary workspace execution.

Monday.com

Use the monday.com MCP server to connect AI agents to your Work OS. Provides secure data access, action tools, and workflow context.

MongoDB

Install the MongoDB MCP Server to query databases and manage Atlas directly from your AI assistant in VS Code, Cursor, and more.

NotebookLM MCP

Use the NotebookLM MCP Server to connect AI agents like Claude to your private docs. Get zero-hallucination answers powered by Gemini.

JustCall

Use the JustCall MCP Server to integrate real-world telephony into your conversational AI agents.

CSS

Connect your AI assistant to the CSS MCP Server to get instant MDN docs and analyze project-wide stylesheet complexity.

Sora

An MCP server to generate and remix videos with OpenAI's Sora 2 API directly from MCP clients like Claude, Cursor, and VS Code.

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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