DuckDuckGo Search
Provides web search capabilities through DuckDuckGo, along with content fetching and parsing features.
Browse 6,627+ Model Context Protocol servers for Claude, Cursor, and VS Code. Find the perfect tools for your AI workflow.
Provides web search capabilities through DuckDuckGo, along with content fetching and parsing features.
Extends the Eino framework with component implementations, callback handlers, and DevOps tools for building LLM applications.
Connects Elasticsearch data to any Model Context Protocol (MCP) Client, enabling natural language interaction.
Showcases Kotlin Multiplatform development through a Fantasy Premier League application.
Provides filesystem operations through the Model Context Protocol, enabling read/write access and directory management.
Provides real-time bus information for Galway, Ireland, built with Kotlin Multiplatform for Android, iOS, and macOS.
Enables programmatic interaction with GitLab repositories and resources via a Model Context Protocol server.
Bypasses search engine anti-scraping mechanisms to execute Google searches and extract results.
Enables creating, reading, updating, and managing Google Sheets spreadsheets programmatically.
Enables interaction with Google Workspace products such as Gmail and Calendar.
Unifies Model Context Protocol (MCP) servers with a consistent backend for local and remote AI integrations.
Connects a local Home Assistant instance to Language Learning Models (LLMs) via the Model Context Protocol (MCP).
Integrates the LINE Messaging API, enabling connection of an AI Agent to a LINE Official Account.
Securely executes code generated by Large Language Models in an isolated and portable environment.
Converts APIs described by OpenAPI specifications into MCP-compatible interfaces for seamless integration with AI agents.
Installs and manages Model Context Protocol (MCP) servers for seamless integration between LLM applications and external data sources.
Aggregates and serves multiple Model Context Protocol (MCP) resource servers through a single HTTP server.
Enables Emacs to interface with MCP servers for structured communication and resource management.
Enables seamless integration of over 650 Model Context Protocol (MCP) servers into agentic frameworks.
Manage and unify multiple MCP server integrations through a single GUI application.
Generates Model Context Protocol (MCP) servers from OpenAPI specifications, enabling AI agents to interact with existing REST APIs.
Enables interaction with OpenSearch clusters using Python.
Analyzes software composition to extract inventory data, detect vulnerabilities, and generate SBOMs.
Enables document format conversion using pandoc through a Model Context Protocol server.
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.