MCP Servers

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

Square

The official Square MCP server that allows AI assistants like Claude Desktop to interact with Square's Connect API.

AppleScript

An MCP server that lets you run AppleScript code to interact with Mac. 

FastAPI

A zero-configuration tool for automatically exposing FastAPI endpoints as Model Context Protocol (MCP) tools.

Exa

An MCP server that lets AI assistants use the Exa AI Search API for web searches.

PixVerse

Official PixVerse MCP server that enables interaction with powerful AI video generation APIs.

Task Master

A task management MCP server for AI-driven development with Claude. Works seamlessly with Cursor AI.

WordPress

An MCP server that enables AI assistants like Claude Desktop to interact with WordPress sites through the WordPress REST API.

Magento

An MCP server that allows you to query product information from a Magento store using Magento 2 REST API.

BrowserStack

BrowserStack's official MCP server that allows you to use their cutting-edge Test Platform directly from your favourite AI tools (MCP clients).

Mobile Next

An MCP server that enables scalable mobile automation, development through a platform-agnostic interface.

Clickhouse

Clickhouse's official MCP server that allows you to interact with your ClickHouse cluster within MCP clients.

XcodeBuild

An MCP server that provides Xcode-related tools for integration with AI assistants and other MCP clients.

Flux UI

An MCP server that helps AI assistants access Flux UI component documentation and examples.

E2B

E2B's official MCP server allows you to add code interpreting capabilities to your Claude Desktop app via the E2B Sandbox.

Braintrust

Braintrust's official MCP server that can read experiment results to help you automatically debug and improve your app.

Obsidian

An MCP server to interact with Obsidian via the Local REST API community plugin.

Tavily

An MCP server that enables AI systems to interact seamlessly with various data sources and tools, facilitating secure, two-way connections.

Context7

An MCP server that pulls up-to-date, version-specific documentation and code examples straight from the source — and places them directly into your prompt.

Azure

Azure's official MCP server that creates a seamless connection between AI agents and key Azure services

Spotify

An MCP server that allows you to connect Claude with Spotify using spotipy-dev's API.

GitMCP

An MCP server that transforms any GitHub repos and pages into a documentation hub.

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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