Litmus
Enables LLMs and intelligent systems to interact with Litmus Edge for device configuration, monitoring, and management.
Browse 6,627+ Model Context Protocol servers for Claude, Cursor, and VS Code. Find the perfect tools for your AI workflow.
Enables LLMs and intelligent systems to interact with Litmus Edge for device configuration, monitoring, and management.
Provides programmatic access to the LittleSis API for tracking corporate power and accountability relationships.
Integrates with the Ticketmaster API to provide real-time event data for AI agents.
Manages and summarizes textual notes, offering a custom URI scheme for resource access and interactive tools.
Provides structured access to Livewire Flux Components documentation from fluxui.dev for AI assistants.
Visually replays LLM chat transcripts with typing animation and playback controls, supporting markdown chat exports.
Enables large language models to seamlessly search, download, and extract information from YouTube music videos.
Enhances the reliability of interactions between Large Language Models (LLMs) and tools, especially MCP servers, by providing a structured, validated, and controllable communicati…
Provides a lean server for fast, surgical access to `llms.txt` documentation, optimizing retrieval for large language models.
Integrates AI assistants with LNbits for seamless management of Lightning Network wallets, payments, and invoices.
Enables natural language queries for Lightning Network node data, providing human-readable responses alongside structured JSON.
Exposes read-only access to LoanPro financial data through various transport protocols.
Simulates a Multi-Context Processing (MCP) system for efficient data handling across multiple contexts.
Enables a self-hosted AI environment on Windows, integrating Ollama, Open WebUI, and MCP for local language model management and chat interaction.
Executes commands and returns structured outputs via an MCP server.
Organizes and manages files across directories by type, leveraging robust permission checking for secure access.
Manages and interacts with local Git repositories using the MCP server framework.
Provides a template repository for running a local language model with an included knowledge base.
Monitors local application logs, providing real-time tailing, error tracking, and search functionalities through a Model Context Protocol (MCP) server.
Enables local RAG systems by registering diverse document formats for advanced search capabilities through an MCP server.
Standardizes local discovery and configuration of MCP Servers by defining a specification for their description.
Provides local speech-to-text transcription using whisper.cpp, optimized for Apple Silicon.
Provides access to various local system utilities through a Model Context Protocol (MCP) server.
Executes Python code locally within Claude Desktop, enabling secure and private experimentation without API keys.
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.