OneSearch
Integrates web search and scraping capabilities using Searxng, Firecrawl, and Tavily.
Browse 6,627+ Model Context Protocol servers for Claude, Cursor, and VS Code. Find the perfect tools for your AI workflow.
Integrates web search and scraping capabilities using Searxng, Firecrawl, and Tavily.
Enables Claude to remember conversations and learn from them over time through a standalone MCP server.
Enables seamless integration with OpenRouter.ai's diverse model ecosystem.
Enables language model assistants to access and search the OpenTofu Registry for information about providers, modules, resources, and data sources.
Automates optimization and analysis using Optuna APIs through the Model Context Protocol.
Provides a Python interface to the Perplexity API for querying responses and managing conversations.
Integrates PiAPI's API with a Model Context Protocol (MCP) server, enabling media content generation from Claude or other MCP-compatible applications.
Analyzes PubMed medical literature to provide researchers with insights into medical research trends.
Runs Python code locally within an interactive REPL environment.
Learn to build AI-infused applications using Quarkus and LangChain4j through a guided workshop.
Enables Claude to create flashcards for spaced repetition learning using the Model Context Protocol.
Executes Python code in a secure, sandboxed environment using an MCP server.
Provides examples for integrating the Model Context Protocol with AWS services.
Empowers AI with real-time desktop vision capabilities and enhanced UI intelligence through the Model Context Protocol.
Provides a standardized Model Context Protocol (MCP) interface for AI assistants to access Google and other search services.
Provides reference information for shadcn/ui components to AI assistants.
Enables AI assistants to efficiently check dependency vulnerability and security information.
Facilitates programmatic interaction with the Spotify API for music search, playback control, and library management.
Empowers developers to build and integrate custom external tools with Puch AI using the Model Context Protocol (MCP).
Simplifies the process of adding Model Context Protocol (MCP) servers to MCP clients with a one-click installation.
Enhances Claude's reasoning capabilities by integrating DeepSeek R1's advanced reasoning engine.
Implements a neural memory system for sequence learning and prediction, drawing inspiration from Google Research's work on generative AI.
Provides a unified interface to numerical, logical, and symbolic solvers, leveraging MCP for problem-solving.
Integrates Vercel's REST API as tools within an MCP server, providing programmatic access to deployment management.
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.