19,306 MCP servers crawled across 12 categories. 164 deterministic detection rules. Evidence chains, not vibes.
一个提供查询鸣潮角色信息、配队、声骸、背景故事及攻略的MCP server 数据来自库街区
MCP服务器用于12306车票查询
Model Context Protocol server for AkShare financial data APIs
A MCP server that enables Claude to discover and call any API endpoint through semantic search. Intelligently chunks OpenAPI specifications to handle large API documentation, with built-in request execution capabilities. Perfect for integrating private APIs with Claude Desktop.
MCP server for the ATTOM Real Estate API
MCP Server Baidu Maps Enhanced - HTTP call Baidu Map API for MCP
MCP server for Bitbucket API operations
Build custom API integrations quickly with this ready-to-use MCP server template. Extend and configure tools, authentication, and API endpoints to suit your needs. Benefit from TypeScript support, unit tests, and built-in pagination and filtering capabilities.
MCP Server for Canteen Data
MCP server for CDEK delivery service API — create orders, track shipments, search delivery points
Auto-generate beautiful changelogs from GitHub PRs using LLM rewriting
A Model Context Protocol (MCP) server for fetching historical currency exchange rates using the Frankfurter API.
Server MCP per interrogare la documentazione di datapizza-ai realizzato con datapizza-ai
MCP server for the Scalyr/DataSet log management API
An MCP Server to interact with the discogs API, supports read operations (for the moment)
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Excel MCP Server & CLI - 23 tools, 214 operations for AI-powered Excel automation via COM API
An MCP server for mcp-server-fear-greed
PowerPoint automation for AI - Slides, Shapes, Text, Charts and more. Windows only.
Open-source MCP server for Zerodha Kite Connect. Portfolio, market data, backtesting, alerts.
MCP server for using the Foxit Cloud API
MCP server for the FXMacroData API — macroeconomic indicators, release calendars, COT, commodities, and forex for AI agents.
MCP server for Gelato print-on-demand API
MCP server implementation for Google's Gemini API
Building MCP servers from scratch requires understanding protocol internals, setting up boilerplate, handling JSON-RPC messaging, and configuring transports. This is repetitive and time-consuming for teams wanting to expose existing APIs or data sources to AI applications.