MCP Server Directory & Submission Hub

The Model Context Protocol (MCP) is a standardized way for AI assistants to interact with external tools and services. This revolutionary protocol enables AI models to access real-world data and perform actions while maintaining security and user control.
Learn more about the protocol →

Why Build on the Model Context Protocol (MCP)?

Enhanced Capabilities
Enable AI assistants to interact with databases, cloud services, and APIs, expanding their ability to help with real-world tasks.
Secure Architecture
Built with security-first design, ensuring controlled access and protecting sensitive information while enabling powerful integrations.
Universal Standard
A unified protocol that works across different AI models and services, creating a consistent and reliable integration experience.
Developer Friendly
Easy to implement and extend, with a growing ecosystem of tools and community-contributed servers for various services.

MCP Servers

Submit your MCP server

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Title *
Description *
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Luma API 2
The Luma API MCP Server provides seamless integration with Luma Labs’ advanced image and video generation models
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Weaviate 93
MCP server for Weaviate vector database
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Tavily 281
The Tavily MCP server provides: Seamless interaction with the tavily-search and tavily-extract tools Real-time web search capabilities through the tavily-search tool Intelligent data extraction from web pages via the tavily-extract tool
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AWS Cost Analysis 2243
MCP server for analyzing AWS service costs and generating cost reports
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ClickHouse 170
An MCP server for ClickHouse.
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21st.dev Magic AI Agent 1296
Magic Component Platform (MCP) is a powerful AI-driven tool that helps developers create beautiful, modern UI components instantly through natural language descriptions. It integrates seamlessly with popular IDEs and provides a streamlined workflow for UI development.
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AWS 73
A lightweight service that enables AI assistants to execute AWS CLI commands through the Model Context Protocol (MCP).
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Perplexity Ask 946
An MCP server implementation that integrates the Sonar API to provide Claude with unparalleled real-time, web-wide research.
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TMDB 25
This MCP server integrates with The Movie Database (TMDB) API to provide movie information, search capabilities, and recommendations.
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Vercel 26
A Model Context Protocol (MCP) integration for Vercel's REST API, providing programmatic access to Vercel deployment management.

Frequently Asked Questions about MCP Servers

What is an MCP server?
An MCP server is a lightweight service that exposes tools and resources to AI assistants via the Model Context Protocol.
How do I publish my MCP server?
Just click that submit button and fill in the form.
Which AI assistants support MCP servers?
Claude Desktop, Cursor IDE, Windsurf, OpenAI Agent SDK, and others that implement the protocol.
MCP vs. OpenAI “function‑calling” — what’s the difference?

Scope: OpenAI function‑calling is an API‑specific JSON protocol that lets ChatGPT call developer‑defined functions inside a single request cycle. MCP is an open, transport‑agnostic protocol that works with any LLM or IDE and supports persistent state, resource streaming and multi‑tool suites.

Transport: Function‑calling occurs over HTTPS. MCP supports STDIO for local processes and Server‑Sent Events for remote servers, enabling CLI‑level latency and bi‑directional progress events.

Security model: Function‑calling inherits the security context of the backend service. MCP adds tool‑level capability descriptors, allowing clients to review & approve each server before use.

Bottom line: choose MCP when you need an open ecosystem where any LLM, IDE or agent framework can reuse the same server; choose function‑calling for quick single‑model prototypes on OpenAI.

Are MCP servers safe to run locally?
Yes—each tool declares required environment variables and permissions up front; clients prompt you to approve before execution. For extra safety, run servers in Docker or point to hosted endpoints.
How does MCP compare to other AI interoperability protocols?
MCP is focused on context and data delivery between AI models and external systems, while protocols like Google’s Agent2Agent (A2A) target agent-to-agent communication. MCP is designed to complement, not replace, other standards in the AI ecosystem.