Discover the Best MCP Servers for AI Agents
Find awesome MCP Servers. Build smarter AI agents.
Browse & Read GitHub Repos via MCP
AI agent for conversation-to-workflows via MCP
Browser Automation for AI Agents via MCP
Powerful GitHub API automation and integration using MCP for developers and AI tools.
Secure, Scalable LLM Integration for Enterprises
Inject version-specific docs and examples directly into LLM conversations, no manual lookup required.
Build, train, and deploy machine learning models directly from your database using SQL.
Power your AI agents with fast, serverless SQL analytics using MotherDuck and the Model Context Protocol.
Give your AI agents semantic memory using Qdrant’s high-performance vector database
Give your AI agents the power to speak and listen using ElevenLabs' lifelike voice synthesis.
Seamlessly integrate JetBrains IDEs with LLM agents using the Model Context Protocol (MCP)
Enable your LLMs to query, visualize, and reason with real-time Grafana data.
Integrates with AI assistants like Claude to enable web search functionality through the Exa AI Search API.
Implements an MCP server for querying and exploring ClickHouse databases.
Seamlessly link Elasticsearch to any MCP client and unlock natural language access to your data.
Enabling Claude to execute code using E2B through the Model Context Protocol (MCP)
Real-Time Web Search for LLMs via Model Context Protocol
Integrate Stripe APIs into AI agents with ease using Python or TypeScript.
Real-Time Web Search Integration for AI Assistants
Automate the web using LLMs and cloud browsers.
No AI Tools found.
Frequently Asked Questions about MCP Server
What is MCP (Model Context Protocol)?
MCP, or Model Context Protocol, is an open-source standard developed by Anthropic that allows AI models like Claude to securely interact with external data, tools, and APIs. It uses a client-server architecture to enable AI assistants to access real-time information and functionality without hardcoding custom integrations. By acting as a bridge between language models and external systems, MCP provides a flexible and scalable way to extend the capabilities of AI through secure, structured context.
What is MCP Server?
An MCP Server is a self-hosted or cloud-based system that supplies context, tools, and external data to AI models through the Model Context Protocol. It allows AI assistants to securely access real-time information from sources like documents, files, databases, and third-party APIs. By acting as a controlled gateway between the AI and external systems, an MCP Server enhances the model’s capabilities without compromising data privacy or requiring direct API key sharing.
How do MCP Server work?
MCP Servers operate using a simple and secure client-server architecture that allows AI models to interact with external tools and data. They expose files, APIs, functions, or other resources through a standardized protocol, enabling seamless communication with AI clients like Claude. Each connection is isolated and secure, typically in a 1:1 session, ensuring that the AI can access only what the host application allows—without exposing sensitive data or credentials.
Is an MCP Server secure for handling APIs and private data?
Yes, MCP Servers are built with strong security principles, making them safe for handling sensitive data and APIs. The MCP protocol ensures that your server retains full control over its own resources and does not need to expose API keys or internal logic to external LLM providers. Authentication and access control are managed directly by the MCP server, maintaining strict data boundaries and minimizing the risk of leakage or unauthorized access. This makes MCP Servers a secure solution for integrating AI with internal tools, private APIs, or enterprise systems.
How to build an MCP Server?
To build an MCP Server, developers create a local or cloud-based service that follows the Model Context Protocol specification. This server defines tools, data sources, or APIs it wants to expose to an AI assistant—such as Claude—using a simple JSON schema over HTTP. The server handles incoming requests, processes them securely, and returns structured responses that the AI can understand. MCP Servers can be written in any language (e.g., Python, Node.js, Go), and are often deployed using Docker for ease of integration. Full documentation is available on the official MCP GitHub to guide you through setup, tool definitions, and security best practices.