找到适合你的 MCP Server，快速完成接入

An educational example demonstrating how to build MCP servers in Python using FastMCP, showing how to expose tools, resources, and prompts to AI clients.

A Model Context Protocol server that enables real-time communication using Server-Sent Events (SSE), providing standardized model management and resource templating capabilities.

A drop-in MCP server implementation for Next.js projects using Vercel MCP Adapter, allowing developers to integrate model context protocol functionality with custom tools, prompts, and resources.

A sample implementation of a Model Context Protocol server using Next.js and the Vercel MCP Adapter, allowing developers to create AI assistants with custom tools and resources.

A template for integrating Model Context Protocol (MCP) servers into Next.js projects using the Vercel MCP Adapter. It enables developers to deploy MCP tools, prompts, and resources as API routes with optional support for Server-Sent Events.

A drop-in Model Context Protocol server implementation for Next.js projects that enables AI tools, prompts, and resources integration using the Vercel MCP Adapter.

Example V2

A simple example of a Model Context Protocol Server implementation

Enables AI assistants to create and manage content collections, perform web searches, and enhance data using Exa AI's websets capabilities with guided workflows for research and analysis.

Enables users to create and interact with hand-drawn Excalidraw diagrams featuring smooth viewport camera control and interactive fullscreen editing. It uses the MCP Apps extension to render collaborative virtual whiteboards directly within supported AI chat interfaces.

Enables users to create and interact with hand-drawn sketches and architecture diagrams directly within chat interfaces using Excalidraw. It leverages the Model Context Protocol to provide interactive HTML visualizations with smooth viewport control and fullscreen editing capabilities.

Enables AI agents to programmatically control a live Excalidraw canvas through element-level CRUD operations and real-time synchronization. It allows agents to iteratively build, inspect, and refine diagrams while providing visual feedback via screenshots and scene descriptions.

Run a live Excalidraw canvas and control it from AI agents.

Generates beautiful Excalidraw diagrams from natural language descriptions using a local llama.cpp LLM, entirely offline.

Turn Excalidraw diagrams into keyframe animations. AI-powered creation via MCP, E2E encrypted sharing, export to MP4/WebM/GIF/SVG.

Enables efficient reading, analyzing, and querying of Excel, CSV, and JSON files with support for chunked processing, column/field filtering, and streaming for large datasets. Supports multiple transport protocols (stdio, HTTP, SSE) for flexible integration.

Enables users to analyze local Excel and CSV files through natural language queries and a web dashboard while keeping data local. It supports saving specific analyses as reusable tools and building a custom analytics toolkit within Claude Desktop.

An MCP server that allows LLMs to read, analyze, and interact with Excel files through file operations, data discovery, and comprehensive analysis tools.

Enables comprehensive Excel operations and financial calculations including investment analysis, rental property management, expense tracking, and automated financial reporting. Supports creating Excel workbooks with advanced financial formulas, cash flow projections, and tax calculations for accounting and finance workflows.

Enables AI agents to create, read, and manipulate Excel files without requiring Microsoft Excel installation. Supports comprehensive spreadsheet operations including formulas, formatting, charts, pivot tables, and data validation.

Enables reading and writing of values, formulas, and formatting in Microsoft Excel files including .xlsx and .xlsm formats. It supports sheet management, table creation, and provides Windows-exclusive features like live editing and screen capture.