CO2 Sensor Simulator
Simulates a CO2 sensor device and provides a JSON-RPC server for interaction, with real and simulated modes.
Browse 6,627+ Model Context Protocol servers for Claude, Cursor, and VS Code. Find the perfect tools for your AI workflow.
Simulates a CO2 sensor device and provides a JSON-RPC server for interaction, with real and simulated modes.
Provides static analysis capabilities to LLMs through the Model Context Protocol (MCP).
Provides advanced code analysis and semantic search capabilities for large codebases, facilitating automatic refactoring and domain understanding.
Enables Large Language Models to create, modify, and delete code via a Model Context Protocol server.
Performs intelligent, AI-powered code audits for security, quality, performance, and more using local Ollama models.
Provides a basic framework for executing code remotely, integrated with a network search tool and API proxy services.
Lints Python and JavaScript code by providing a REST API that returns a lint report in JSON format.
Scans codebases for definitions, respecting .gitignore and outputting LLM-friendly formats.
Summarizes code files within a directory using Gemini Flash 2.0, and offers an MCP server for LLM tool integration.
Automates Swagger documentation generation and seamlessly integrates with ApiFox, allowing AI assistants to directly manage and upload API definitions.
Generates code call graphs and serves them via the Model Context Protocol (MCP) for AI application integration.
Analyzes Python code using AST, stores information in Weaviate, and provides querying via an MCP server.
Provides an efficient way to transfer your project's code context into Large Language Models, respecting common ignore rules and handling large codebases.
Equips AI coding assistants with comprehensive, up-to-date knowledge of large codebases, preventing errors and redundant training.
Analyzes Codecov reports to suggest tests and identify areas lacking test coverage.
Optimizes understanding of complex codebases by generating detailed call graphs, identifying critical elements, and providing semantic search capabilities.
Accesses the Codeforces API to provide comprehensive contest, user, and submission data through a standardized MCP interface.
Indexes large codebases and queries code graphs with natural language using a high-performance GraphRAG implementation.
Enables AI assistants to analyze, search, and navigate codebases with precision.
Manages AWS CodePipeline deployments through Windsurf and Cascade.
Enhances interaction between Claude and code by providing tools for code analysis, manipulation, and testing.
Automates project management, POU creation, code editing, and compilation tasks within the CODESYS V3 programming environment via Model Context Protocol (MCP).
Provides Model Context Protocol (MCP) server implementations, enabling seamless integration with various services.
Provides context-aware code assistance, explanations, and suggestions directly related to local project files.
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.