Find the Perfect MCP Server for Claude, Cursor & AI Tools
The most comprehensive Model Context Protocol (MCP) directory with 775+ verified servers. Connect your AI applications to databases, APIs, and external tools in minutes.
What is Model Context Protocol (MCP)?
Model Context Protocol is an open standard developed by Anthropic that enables AI models like Claude to securely connect with external data sources and tools. Instead of building custom integrations for each service, MCP provides a universal interface for AI applications.
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MCP Servers(775 servers)
Everything
ReferenceReference / test server with prompts, resources, and tools.
Fetch
ReferenceWeb content fetching and conversion for efficient LLM usage.
Filesystem
ReferenceSecure file operations with configurable access controls.
Git
ReferenceTools to read, search, and manipulate Git repositories.
Memory
ReferenceKnowledge graph-based persistent memory system.
Sequential Thinking
ReferenceDynamic and reflective problem-solving through thought sequences.
Time
ReferenceTime and timezone conversion capabilities.
Agentset
OfficialRAG for your knowledge base connected to [Agentset](https://agentset.ai).
Algolia
OfficialUse AI agents to provision, configure, and query your [Algolia](https://algolia.com) search indices.
CB Insights
OfficialUse the [CB Insights](https://www.cbinsights.com) MCP Server to connect to [ChatCBI](https://www.cbinsights.com/chatcbi/)
FalkorDB
OfficialFalkorDB graph database server get schema and read/write-cypher [FalkorDB](https://www.falkordb.com)
IBM wxflows
OfficialTool platform by IBM to build, test and deploy tools for any data source
OMOP MCP
OfficialMap clinical terminology to OMOP concepts using LLMs for healthcare data standardization.
PaddleOCR
OfficialAn MCP server that brings enterprise-grade OCR and document parsing capabilities to AI applications.
Pagos
OfficialInteract with the Pagos API. Query Credit Card BIN Data with more to come.
Patronus AI
OfficialTest, evaluate, and optimize AI agents and RAG apps
PostHog
OfficialInteract with PostHog analytics, feature flags, error tracking and more with the official PostHog MCP server.
Postman API
OfficialManage your Postman resources using the [Postman API](https://www.postman.com/postman/postman-public-workspace/collection/i2uqzpp/postman-api).
Quickchat AI
OfficialLaunch your conversational [Quickchat AI](https://quickchat.ai) agent as an MCP to give AI apps real-time access to its Knowledge Base and conversational capabilities
Raygun
OfficialInteract with your crash reporting and real using monitoring data on your Raygun account
Routine
OfficialMCP server to interact with [Routine](https://routine.co/): calendars, tasks, notes, etc.
Vectorize
Official[Vectorize](https://vectorize.io) MCP server for advanced retrieval, Private Deep Research, Anything-to-Markdown file extraction and text chunking.
ZenML
OfficialInteract with your MLOps and LLMOps pipelines through your [ZenML](https://www.zenml.io) MCP server
1mcpserver
OfficialMCP of MCPs. Automatically discover, configure, and add MCP servers on your local machine.
1Panel
OfficialMCP server implementation that provides 1Panel interaction.
A2A
OfficialAn MCP server that bridges the Model Context Protocol (MCP) with the Agent-to-Agent (A2A) protocol, enabling MCP-compatible AI assistants (like Claude) to seamlessly interact with A2A agents.
Ableton Live
Officialan MCP server to control Ableton Live.
Actor Critic Thinking
OfficialActor-critic thinking for performance evaluation
ActivityPub MCP
OfficialA comprehensive MCP server that enables LLMs to explore and interact with the Fediverse through ActivityPub protocol, supporting actor discovery, timeline fetching, instance exploration, and WebFinger resolution across decentralized social networks.
ADR Analysis
OfficialAI-powered Architectural Decision Records (ADR) analysis server that provides architectural insights, technology stack detection, security checks, and TDD workflow enhancement for software development projects.
Frequently Asked Questions About Model Context Protocol
What is a Model Context Protocol (MCP) server?
A Model Context Protocol (MCP) server is a standardized interface that enables AI models to access external data and tools. MCP servers provide a consistent way for large language models (LLMs) to interact with databases, APIs, and other services, enhancing their capabilities beyond their training data.
How do I choose the right MCP server for my project?
When selecting an MCP server, consider your specific use case, required features (like database access, API integration, or tool execution), performance needs, and compatibility with your existing AI infrastructure. Our directory provides detailed information on each server's capabilities, allowing you to filter and compare options based on your requirements.
How is MCP different than an API?
While both MCP and APIs facilitate data exchange between systems, they serve fundamentally different purposes. Traditional APIs are designed for human developers to integrate services using predefined endpoints and documentation. In contrast, MCP is specifically engineered for AI models to dynamically discover, understand, and utilize external tools and data sources without human intervention. Key differences include: MCP provides semantic descriptions of capabilities that AI models can interpret, offers a standardized pattern for AI-to-tool communication across different services, includes built-in context management to maintain state across interactions, and can dynamically expose only relevant tools based on context, permissions, and user needs.
How can I contribute my own MCP server to this directory?
We welcome contributions from the community! If you've developed an MCP server, you can submit it for inclusion in our directory by providing details about its features, capabilities, and implementation. Contact us through the submission form or GitHub repository to start the process of adding your server to our comprehensive listing.
What are the benefits of using the Model Context Protocol?
The Model Context Protocol offers numerous advantages, including standardized access to external data sources, improved AI capabilities through tool use, consistent interfaces across different models, enhanced security through controlled access patterns, and greater flexibility in AI application development. MCP enables AI models to perform more complex tasks by providing them with the ability to interact with the outside world in a structured manner.