MCP Servers

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

Gitingest

Integrate Gitingest-MCP with your MCP client to access repository summaries, directory structures, and file contents from GitHub.

MCP Feedback Enhanced

An MCP server for human-in-the-loop AI, reducing costs with Qt/Web UIs and image support.

Vibetest Use

An MCP server for automated website testing using multiple browser agents. Detect UI bugs, broken links, and accessibility issues.

Graphiti

Connect AI assistants to Graphiti's temporal knowledge graphs via MCP protocol. Build agents with persistent memory and dynamic context understanding.

Buttplug

Use buttplug-mcp to query/control intimate devices (LELO, Lovense) through AI tools. MIT-licensed, with Homebrew installs and Ollama support.

CodeMCP

Turn Claude Pro into a powerful coding agent with codemcp - direct file editing, Git versioning, and IDE-agnostic development workflow integration.

Unity

An MCP server enabling AI assistants to manage Unity Editor tasks, automate workflows, and edit C# scripts.

UniProt

Access UniProt protein database through 26 specialized bioinformatics tools via Model Context Protocol for advanced protein research and analysis.

MCP Code Executor

MCP Code Executor enables LLMs to run Python code in Conda/venv environments. Supports incremental generation and dependency management.

Google Analytics

Connect Google Analytics 4 to Claude and AI clients. Query 200+ GA4 metrics and dimensions using natural language with this MCP server.

Clojure

An MCP server that connects AI models to your Clojure nREPL for enhanced, REPL-driven development with specialized editing tools.

Bright Data

Enhance AI agents with Bright Data MCP Server. Access real-time web data, bypass restrictions, and integrate with MCP clients.

Desktop Commander

Extend Claude's capabilities with Desktop Commander MCP. Execute commands, edit code, and manage files directly from your AI assistant using Model Context Protocol.

MindsDB

Connect, query, and analyze data from multiple sources with MindsDB. Features SQL interface, AI agents, and automated job scheduling.

HeyBeauty Virtual TryOn

Implement virtual clothing try-on in MCP clients with HeyBeauty MCP Server. Features include resource management, task handling, and LLM prompt generation.

Sympy

A symbolic mathematics MCP server to integrate, differentiate, solve equations, and perform tensor calculus in your language model interactions.

Robinhood

Simplify your Robinhood crypto trading with this MCP Server. Offers REST API, WebSocket support, and easy configuration for developers.

Peekaboo

An MCP Server enables your AI assistant to analyze content on macOS and capture screenshots with high precision.

DAX Formatter

Enhance your data analysis with the DAX Formatter MCP Server. Leverage SQLBI's formatting engine for clean, consistent DAX code.

Apple Notes

Integrate Apple Notes with AI assistants using Notes MCP. This MCP server provides access to your notes, enhancing productivity and organization.

DeepWiki Official

DeepWiki’s official MCP server that provides programmatic access to GitHub repository documentation and AI-powered search capabilities. Features Use Cases How to Use It 1. Base Server URL: 2. Available Tools: 3. Wire Protocols: 4. Usage Tips: FAQs Q: Do…

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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