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Enable AI assistants to explore and query your Tailpipe data using natural language.
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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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Tailwind Gemini
CommunityGenerates AI-powered CSS and integrates Tailwind CSS into various development environments.
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Tally
CommunityManages Tally.so forms and submissions using AI-driven natural language commands and secure bulk operations.
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Task Portal
CommunityEnables self-evolving, general problem-solving through logical reasoning, ethical constraints, and adaptable solutions.
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Task Trellis
CommunityProvides a Model Context Protocol server for Task Trellis, enabling task management for AI coding agents.
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Taskfile Server
CommunityExposes Taskfile tasks as standardized tools for AI assistants.
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TaskFlow
CommunityStreamlines AI assistant workflows by breaking down user requests into manageable tasks with dependencies and approval steps.
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TaskMateAI
CommunityManages and executes tasks autonomously using AI through the Model Context Protocol (MCP).
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TaskNote Bridge
CommunityIntegrates Things 3 and Apple Notes with AI assistants using the Model Context Protocol (MCP).
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TasksForge
CommunityBridges AI coding agents with Tasksforge.ai to provide dynamic project context and manage implementation progress.
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Taxi Callcenter Agents
CommunityFacilitates taxi ordering through a call center agent.
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TeamCity
CommunityExposes JetBrains TeamCity as structured AI-ready resources and tools for large language model (LLM) agents and integrated development environment (IDE) plugins.
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TeamSpeak
CommunityControl a TeamSpeak server from AI models using the Model Context Protocol.
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Teamwork AI
CommunityExtends Teamwork.com with AI capabilities via the Model Context Protocol (MCP).
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TechDebtMaster
CommunityAutomates the discovery, triage, and resolution of technical debt within software repositories through AI-powered analysis.
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TechMCP
CommunityIntegrates a Model Context Protocol (MCP) server with PSG College of Technology's e-campus portal, providing AI assistants access to student academic data.
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Telegram Assistant
CommunityConnects AI agents to Telegram for real-time notifications, interactive action approvals, and progress tracking.
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Template Generator
CommunityGenerates enterprise software templates and automates workflows for digitalization processes.
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Tempo
CommunityEnables AI assistants to query and analyze distributed tracing data from Grafana Tempo.
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Tempo Filler
CommunityEnables AI assistants to interact with JIRA's Tempo time tracking system.
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Temporal Context
CommunityEmpowers AI assistants to adapt their responses and behavior intelligently based on the user's schedule and routines.
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Tencent Cloud
CommunityIntegrates Tencent Cloud services with AI applications for enhanced cloud-native development, infrastructure management, and AI-assisted workflows.
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Tensorone
CommunityEnables decentralized AI agent development and GPU computing within a Web3 Dapp environment.
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Terminal.shop
CommunityEnables AI assistants to interact with Terminal.shop for product browsing, cart management, order placement, and subscription handling.
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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