Agile Flow
Manages AI-assisted agile development workflows using a resource-based API.
Browse 6,627+ Model Context Protocol servers for Claude, Cursor, and VS Code. Find the perfect tools for your AI workflow.
Manages AI-assisted agile development workflows using a resource-based API.
Enables Large Language Models (LLMs) to find Agoda hotel reviews.
Connect AI assistants to a universal product search engine for discovering and purchasing products across the web.
Provides a Python-based server for efficient interaction with the Aha! API using FastMCP and HTTP streaming.
Leverages AI models and resource sets for tasks like semantic search, relationship extraction, and context-aware question answering.
Enables AI agents to perform security analysis by detecting and redacting sensitive information in text content.
Provides a suite of specialized servers to enhance AI system capabilities with advanced mapping, autonomous browsing, and deep research functionalities.
Orchestrates a collection of AI agents for web scraping, real-time information retrieval, and dynamic podcast generation.
Provides a beginner-friendly guide to creating self-operating AI agents using Python, the OpenAI API, and ReAct prompts.
Securely connect Claude Code to OpenAI and Google Gemini large language models with advanced content and prompt security.
Automate the logging and management of AI assistant conversation history with developers.
Demonstrates building and utilizing Model-Computer Protocol (MCP) Client and Server components to facilitate an end-to-end process from user query to large model response, incorpo…
Unifies multiple AI services for streamlined analysis and standardized output formats.
Enables web search capabilities within an MCP environment using the Tavily API.
Analyzes security threats using AI-powered natural language to SQL conversion on an MCP Server.
Gathers and processes premier global AI information for integration into LLM conversations.
Connects GPT-4 to a backend merchant store API for querying and updating store data via the Model Context Protocol.
Provides AI-powered visual analysis capabilities for web applications using the Gemini Vision API.
Generates Model Context Protocol (MCP) programs from OpenAPI Specification (OAS) files.
Provides a curated collection of articles and guides on AI, Generative AI, and Machine Learning.
Provides a lightweight, MCP-compatible server built with Bun.js and Elysia for advanced AI integrations.
Establishes an MCP server leveraging the Tavily API for robust web search functionalities.
Demonstrates integration of Azure OpenAI-based AI agents with Microsoft Fabric data warehouses through GraphQL using the Model Context Protocol (MCP).
Provides AI-powered security analysis and safety instructions to protect AI agents during Model Context Protocol (MCP) interactions.
MCP (Model Context Protocol) servers are tools that connect AI assistants like Claude, Cursor, and other AI coding tools to external data sources, APIs, and services. They enable AI models to access real-time information, execute code, interact with databases, and perform actions beyond their base capabilities. Check out our curated collection of the best MCP servers for Cursor →
MCP servers act as intermediaries between AI assistants and external systems. When you ask Claude or Cursor to perform a task requiring external data, the MCP server handles the request, fetches the data, and returns it in a format the AI can understand and use.
When selecting an MCP server, consider:
For detailed setup guides, see our Complete MCP Server Setup Guide →
Everything you need to know about installing, using, and troubleshooting Model Context Protocol.
To install MCP servers in Cursor: 1) Open Cursor settings (Cmd+,), 2) Navigate to MCP Servers section, 3) Click 'Add Server', 4) Enter server configuration (name and command), 5) Restart Cursor. Check out our curated collection of the best MCP servers for Cursor with detailed setup guides.
MCP servers are protocol-level integrations that work across multiple AI assistants (Claude, Cursor, etc.), while VS Code extensions are editor-specific. MCP servers provide context to AI models, whereas extensions add IDE functionality. MCP is more portable and AI-focused.
Most MCP servers listed are open-source and free. Some enterprise connectors (like Salesforce or Jira) may require paid API access to the underlying service, but the MCP server code itself is usually free.
Yes! The Model Context Protocol is open standard. You can build servers in Python or TypeScript/Node.js to expose your own internal APIs or databases to Claude and Cursor.
An MCP server is a lightweight program that exposes specific capabilities (data access, tool execution, prompts) to AI assistants through a standardized protocol. MCP servers allow Claude and other AI models to securely interact with external systems like databases, APIs, file systems, and web services while maintaining security boundaries.
MCP servers connect to Claude Desktop or other MCP-compatible clients through a standardized JSON-RPC protocol. When you configure an MCP server in Claude Desktop, Claude can access its resources (data/content), use its tools (functions to execute), and leverage its prompts (templated interactions). The protocol ensures secure, controlled access to external capabilities.
Essential MCP servers for beginners include: Filesystem (local file access), SQLite/PostgreSQL (database queries), GitHub (repository access), Brave Search (web search), and Memory (persistent context). These cover common use cases and have excellent documentation. Start with 2-3 servers and expand based on your needs.
Most MCP servers install via npm (npx) or Python (uvx/pip). Configure them in Claude Desktop's config file (claude_desktop_config.json) by specifying the command, arguments, and any required environment variables like API keys. Our directory links to each server's documentation with specific installation instructions.
MCP servers run locally on your machine with explicit permissions you configure. They can only access resources you authorize, and Claude cannot bypass these boundaries. However, always review server code, use official/verified servers when possible, limit permissions to what's necessary, and be cautious with servers that request broad access.
Resources are data sources Claude can read (files, database results, API responses). Tools are functions Claude can execute (run queries, send messages, create files). Prompts are pre-defined templates for common interactions. Most MCP servers expose one or more of these capability types.
MCP is supported by Claude Desktop (official), Cursor IDE, Continue (VS Code extension), and various community implementations. The protocol is open, so support is expanding to more AI development tools. Check our compatibility filters to find servers tested with your preferred client.
Our MCP servers directory is updated weekly with new servers, documentation improvements, and compatibility updates. We monitor GitHub releases, community contributions, and Anthropic announcements to ensure our listings reflect the latest MCP ecosystem developments.