PIProcess Intelligence
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Provides deep insight into system and running processes, enabling local LLM agents to analyze machine state and offer real-time solutions.
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MCP Server Directory
Browse 6,627+ Model Context Protocol servers for Claude, Cursor, and VS Code. Find the perfect tools for your AI workflow.
MCP Servers listings
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Processing
CommunityEnables AI assistants to create and run Processing sketches directly using natural language commands.
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Procrastinator
CommunityProvides task management capabilities by bridging AI interaction with a Laravel application.
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ProdE
CommunityProvides deep contextual understanding of multiple codebases to enhance AI coding assistants and development workflows.
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Project Handoffs
CommunityFacilitates persistence over AI-coding-agent sessions by managing handoffs and tracking next steps in projects.
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Project Memory
CommunityEnables AI agents to persistently store and retrieve project information from a memory file.
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ProjectDocHelper
CommunityAutomatically generates project documentation and serves it via MCP to improve AI development tool accuracy.
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Prompt
CommunityManages AI prompts, converting them into reusable tools for MCP clients.
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Prompt Auto-Optimizer
CommunityAutomatically evolve and optimize AI prompts to improve performance, creativity, and reliability.
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Prompt New
CommunityManages and saves AI prompts with timestamps via a Model Context Protocol server.
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Prompt Optimizer
CommunityOptimizes AI prompts with high-performance real-time streaming, pattern-based enhancement, and comprehensive session management capabilities.
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PromptLab
CommunityTransforms basic user queries into optimized prompts for AI systems by leveraging a modular, template-based approach.
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Promptopia
CommunityEnables AI applications to manage, organize, and reuse prompt templates through the Model Context Protocol.
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Promptz
CommunityAccess prompts from promptz.dev directly within AI assistants for streamlined development.
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Proton Drive
CommunityEnables AI assistants to securely interact with your Proton Drive files and folders.
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Provenance Monitor
CommunityMonitors the software supply chain provenance of local Model Context Protocol (MCP) server packages.
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Puch AI Starter
CommunityFacilitates the creation of custom Model Context Protocol (MCP) servers, enabling AI assistants like Puch AI to securely interact with external tools and data sources.
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PuchAI
CommunityProvides a foundational backend server for specialized application needs.
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PuchAI Hackathon
CommunityRenders various data plots, scrapes Reddit for market insights, retrieves medicine information, and provides persistent memory for user preferences and tasks.
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PureScript Tools
CommunityEnables AI assistants to access comprehensive PureScript development and analysis capabilities through a Model Context Protocol (MCP) server.
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Py GCalendar
CommunityEnables seamless access and management of Google Calendar events via a Model Context Protocol (MCP) server.
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Pydantic AI Docs
CommunityProvides programmatic access to Pydantic-AI documentation via a Model Context Protocol (MCP) server.
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PyRag
CommunityProvides AI coding assistants with access to current, comprehensive Python library documentation, eliminating frustration from outdated examples and incorrect information.
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Pyrefly Autotype
CommunityAutomates Python type inference and checking for AI coding assistants using the Pyrefly engine.
What Are MCP Servers?
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 →
How MCP Servers Work
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.
Types of MCP Servers
- Code Completion Servers: Enhance AI coding assistants with context from your codebase, documentation, and project structure.
- API Integration Servers: Connect AI to REST APIs, GraphQL endpoints, and web services for real-time data access.
- File Management Servers: Enable AI to read, write, and organize files in your development environment.
- Data Analysis Servers: Connect AI to databases, data warehouses, and analytics tools.
Choosing the Right MCP Server
When selecting an MCP server, consider:
- IDE Compatibility: Ensure it works with Cursor, VS Code, or your preferred editor
- Use Case Match: Choose servers designed for your specific needs
- Maintenance Status: Prefer actively maintained servers with recent updates
- GitHub Activity: Higher stars and recent commits indicate quality
Popular Use Cases
- AI-Powered Code Completion Enhance Cursor and Claude with project-specific context.
- Automated API Testing Connect AI to your APIs for intelligent testing and debugging.
- Database Querying Let AI write and execute SQL queries against your databases.
For detailed setup guides, see our Complete MCP Server Setup Guide →
Frequently Asked Questions About MCP Servers
Everything you need to know about installing, using, and troubleshooting Model Context Protocol.
How do I install MCP servers in Cursor?
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.
What's the difference between MCP servers and VS Code extensions?
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.
Are these MCP servers free to use?
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.
Can I build my own MCP server?
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.
What is a Model Context Protocol (MCP) server?
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.
How do MCP servers work with Claude?
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.
What are the best MCP servers to start with?
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.
How do I install and configure MCP servers?
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.
Are MCP servers secure?
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.
What's the difference between MCP resources, tools, and prompts?
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.
Which AI clients support MCP besides Claude Desktop?
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.
How often is this MCP servers directory updated?
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.
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