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mcp server

A directory for discovering and sharing Model Context Protocol (MCP) Servers.

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MCP Model Context Protocol AI
More categories (8)
Artificial intelligence Claude AI tools API Server Integration Data source LLM
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Verdict

A quick read before you dig into the full page.

Best if
You are aI developers and need: Enhancing AI capabilities by connecting to external data sources and tools
Pick this when
A directory for discovering and sharing Model Context Protocol (MCP) Servers

Overview

MCP Servers is a platform that collects and organizes Model Context Protocol (MCP) Servers. It serves as a central directory where users can discover, share, and learn about various MCP Servers available for AI applications. MCP Servers enhance AI capabilities by providing access to external data, tools, and prompts through a client-server architecture, enabling AI systems like Claude to securely connect with various data sources.

mcp server screenshot

Best for

Short scenarios where mcp server tends to shine.

  • Enhancing AI capabilities by connecting to external data sources and tools

    Best for AI developers

  • Integrating AI models with various services like databases, APIs, and file systems

    Best for Machine learning engineers

  • Building AI applications that can access real-time information and perform actions

    Best for Data scientists

  • Connecting Claude to MCP servers to access external data sources and tools

    Best for AI researchers

How it works

Users can search and discover MCP servers on the mcp.so website. They can explore featured, latest, official, and innovative MCP servers. To submit a server, users can create a new issue in the GitHub repository, providing details about the server's name, description, features, and connection information.

Example outputs 1

Sample generations and before/after results for mcp server.

mcp server screenshot
mcp server screenshot

Access & connections

Where you can use mcp server, and what it plugs into.

Platforms

Web

Integrations

MCP

Capabilities

What this tool accepts, produces, and is strongest at.

Highlights

  • Collection of MCP Servers
  • Search and discovery of MCP Servers
  • MCP Client directory
  • Community platform for sharing and learning about MCP Servers

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In the news

Launch coverage and community mentions for mcp server.

FAQ 6

What is MCP Servers?
MCP Servers is a platform that collects and organizes Model Context Protocol (MCP) Servers. It serves as a central directory where users can discover, share, and learn about various MCP Servers available for AI applications. MCP Servers enhance AI capabilities by providing access to external data, tools, and prompts through a client-server architecture, enabling AI systems like Claude to securely connect with various data sources.
How to use MCP Servers?
Users can search and discover MCP servers on the mcp.so website. They can explore featured, latest, official, and innovative MCP servers. To submit a server, users can create a new issue in the GitHub repository, providing details about the server's name, description, features, and connection information.
What is MCP (Model Context Protocol)?
MCP is an open-source protocol developed by Anthropic that enables AI systems like Claude to securely connect with various data sources. It provides a universal standard for AI assistants to access external data, tools, and prompts through a client-server architecture.
What is MCP Server?
MCP Server is a system that provides context, tools, and prompts to AI clients. It can expose data sources like files, documents, databases, and API integrations, allowing AI assistants to access real-time information in a secure way.
How can I submit my MCP Server to mcp.so?
You can submit your MCP Server by creating a new issue in our GitHub repository. Click the 'Submit' button in the navigation bar or visit our GitHub issues page directly. Please provide details about your server including its name, description, features, and connection information.
Is MCP Server secure?
Yes, security is built into the MCP protocol. Server controls its own resources, there's no need to share API keys with LLM providers, and the system maintains clear boundaries. Each server manages its own authentication and access control.
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