MCP Servers

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

Crawl4AI RAG

A powerful MCP server that integrates with Crawl4AI and Supabase to provide AI agents and AI coding assistants with advanced web crawling and RAG capabilities.

ROS 2

A Python-based MCP server that enables AI assistants and MCP clients to control robots via ROS 2 topics.

Neovim

An MCP server that connects MCP clients like Claude Desktop to Neovim and the official neovim/node-client JavaScript library.

Svelte Docs

An MCP server that provides a comprehensive reference guide for Svelte. This helps LLMs provide accurate guidance when users are working with Svelte.

MySQL

An MCP server that provides read-only access to MySQL databases.

Reprompter

An MCP server that analyzes prompts, compares them with relevant data, and uses OpenRouter (Claude 3.7 Sonnet) to refactor them.

Deepwiki

An MCP server for Deepwiki that takes a Deepwiki URL via MCP, crawls all relevant pages, converts them to Markdown, and returns either one document or a list by page.

Imagegen

 An MCP server that acts as a wrapper around OpenAI's Image Generation and Editing APIs.

Salesforce

Salesforce's official MCP server that allows you to interact with your Salesforce data and metadata using natural language.

Dev.to

An open source MCP server that allows AI assistants to access and interact with Dev.to content.

Reaper

An MCP server that enables AI agents to create fully mixed and mastered tracks in REAPER with both MIDI and audio capabilities.

Vantage

An MCP server that uses natural language to explore your organization’s cloud costs via MCP clients, like Claude, Cursor, and others.

Groq Compound

Groq's official MCP server that enables you to interact with Groq models, including compound/meta models.

Memory Bank

An MCP server that helps teams create, manage, and access structured project documentation.

Tool Chainer

An MCP server that chains calls to other MCP tools, reducing token usage by allowing sequential tool execution with result passing.

Vibe Eyes

An MCP server that enables LLMs to "see" what's happening in browser-based games and applications.

PlayCanvas Editor 

An open-source MCP Server for automating the PlayCanvas Editor using an LLM.

Game Asset

An open-source MCP server for creating 2D/3D game assets from text using Hugging Face AI models.

Jupyter

An MCP server that provides interaction with Jupyter notebooks running in any JupyterLab.

Razorpay

The official Razorpay MCP server provides seamless integration with Razorpay APIs, enabling advanced payment processing capabilities for developers and AI tools.

Sandbox

An MCP server that enables MCP clients to run code in secure, isolated Docker containers.

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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