MCP Servers

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

Anki

An MCP server enabling programmatic control of Anki flashcards. Automate card creation, updates, and reviews.

Nutrient DWS

Enable AI-driven document manipulation with Nutrient DWS MCP Server. Merge, edit, secure, and optimize PDFs using natural language commands.

Strands Agents

Integrate with 40+ MCP-compatible apps and leverage Strands Agents SDK documentation.

Hyper

A modular MCP server using WebAssembly plugins. Extend your AI workflows with custom tools written in any Wasm-compatible language.

Terraform

Enhance your Infrastructure as Code development with Terraform MCP Server. Automate provider discovery, explore modules, and streamline your Terraform workflow.

Manim

Create, render, and retrieve mathematical animations remotely using Python scripts.

AWS

Integrate AWS services into your AI workflows using MCP Servers. Boost development speed and reduce errors in cloud-native projects.

Browser Use

Automate web tasks using AI with Browser-Use MCP Server. Supports popular AI clients, SSE transport, and secure Docker deployment.

macOS Automator

Execute AppleScript and JXA remotely with macOS Automator MCP Server.

gpt-image-1

Integrate OpenAI's gpt-image-1 model into your MCP workflow with this server for AI-powered image generation and editing. Simplify your image creation process.

TaskFlow

Optimize AI task management with TaskFlow MCP. Break down requests, manage dependencies, and ensure user control in your development projects.

AgentQL

Simplify web scraping with AgentQL MCP Server. Extract data from any website using AI-powered tools and natural language commands.

MistTrack

MistTrack MCP Server connects Claude AI to blockchain data, offering tools for asset tracking, risk assessment, and transaction analysis. Ideal for developers and analysts.

Claude Code

Speedup your coding process with Claude Code MCP Server. Automate refactoring, bug fixing, and prototyping using advanced AI capabilities.

Claude Web Search

Integrate real-time web search into LLMs with Claude Web Search MCP Server.

OpenMemory

A local-first MCP server for managing AI interactions across tools. Store, organize, and control your context with privacy and flexibility.

Strava

Integrate Strava data into AI assistants with this MCP server. Access activities, segments, and routes programmatically for enhanced fitness analysis and planning.

Kokoro Text to Speech

Implement text-to-speech with Kokoro TTS MCP Server. Generate MP3s, manage local storage, and integrate with S3. Ideal for developers needing audio output.

Airbnb

Simplify Airbnb data access with this MCP Server. Search listings, get detailed info, and integrate easily with JSON output. Ideal for developers and analysts.

DataForSEO

Integrate AI agents with DataForSEO APIs using this TypeScript MCP server. Access SERP, keyword, and on-page SEO data through a standardized interface.

Nobitex Market Data

An MCP server that provides access to cryptocurrency market data from the Nobitex API.

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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