Discover Awesome MCP Servers

Search Intent MCP

一个基于 MCP 的服务，用于分析用户搜索关键词以确定其意图，并提供分类、推理、参考资料和搜索建议，以支持 SEO 分析。

Nearest Tailwind Colors

Finds the closest Tailwind CSS palette colors to any given CSS color value. Supports multiple color spaces and customizable result filtering to help match designs to Tailwind's color system.

CozoDB Memory MCP Server

Local-first memory for Claude & AI agents with hybrid search, Graph-RAG, and time-travel, runs entirely on your machine.

PushCI

AI-native, zero-config CI/CD. Detects 33 languages + 40 frameworks, generates pipelines, runs locally at $0 cloud cost, diagnoses failures with AI, and deploys to 20 targets.

doc-tools-mcp

在 Node.js 中实现 Word 文档的读取和写入 MCP (Message Communication Protocol) 协议，需要分解成几个部分，并使用合适的库来处理 Word 文档和网络通信。 由于 MCP 通常指的是消息通信协议，与 Word 文档本身没有直接关系，因此我将假设你需要： 1. **读取和写入 Word 文档 (docx 格式)** 2. **使用 MCP 协议发送和接收 Word 文档的内容或相关信息** 以下是一个概念性的实现方案，包含代码示例和解释。 **1. 读取和写入 Word 文档 (docx 格式)** 可以使用 `docx` 库来处理 Word 文档。 ```bash npm install docx ``` ```javascript const { Document, Packer, Paragraph, TextRun } = require("docx"); const fs = require("fs"); // 创建一个新的 Word 文档 async function createWordDocument(data) { const doc = new Document({ sections: [{ children: [ new Paragraph({ children: [ new TextRun(data), ], }), ], }], }); // 将文档保存到文件 const buffer = await Packer.toBuffer(doc); fs.writeFileSync("my-document.docx", buffer); console.log("Word document created successfully!"); } // 读取 Word 文档 (需要额外的库，例如 mammoth.js) async function readWordDocument(filePath) { const mammoth = require("mammoth"); // 需要安装: npm install mammoth try { const result = await mammoth.extractRawText({ path: filePath }); const text = result.value; console.log("Word document content:", text); return text; } catch (error) { console.error("Error reading Word document:", error); return null; } } // 示例用法 async function main() { await createWordDocument("Hello, this is a test document created with Node.js!"); const content = await readWordDocument("my-document.docx"); if (content) { console.log("Successfully read the document."); } } main(); ``` **解释:** * **`docx` 库:** 用于创建和修改 Word 文档。 `Document`, `Paragraph`, `TextRun` 是 `docx` 库提供的类，用于构建文档结构。 * **`mammoth` 库:** 用于读取 Word 文档的内容。 `mammoth.extractRawText` 提取文档中的文本。 * **`fs` 模块:** Node.js 的文件系统模块，用于读写文件。 * **`createWordDocument` 函数:** 创建一个包含指定文本的 Word 文档，并将其保存到 `my-document.docx` 文件中。 * **`readWordDocument` 函数:** 读取指定路径的 Word 文档，并返回其文本内容。 **2. 使用 MCP 协议发送和接收 Word 文档的内容或相关信息** 这里需要定义 MCP 协议的具体格式。 假设 MCP 协议包含以下字段： * `type`: 消息类型 (例如 "document_content", "document_metadata") * `data`: 消息数据 (例如 Word 文档的内容，文档的元数据) 可以使用 Node.js 的 `net` 模块创建 TCP 服务器和客户端，并使用自定义的 MCP 协议进行通信。 ```javascript const net = require("net"); // MCP 协议编码函数 function encodeMCP(type, data) { const message = JSON.stringify({ type, data }); const length = Buffer.byteLength(message, 'utf8'); const lengthBuffer = Buffer.alloc(4); // 4 字节表示消息长度 lengthBuffer.writeInt32BE(length, 0); return Buffer.concat([lengthBuffer, Buffer.from(message, 'utf8')]); } // MCP 协议解码函数 function decodeMCP(buffer) { const length = buffer.readInt32BE(0); const message = buffer.slice(4, 4 + length).toString('utf8'); return JSON.parse(message); } // 服务器端 function startServer(port) { const server = net.createServer((socket) => { console.log("Client connected."); let receivedData = Buffer.alloc(0); socket.on("data", (data) => { receivedData = Buffer.concat([receivedData, data]); while (receivedData.length >= 4) { const length = receivedData.readInt32BE(0); if (receivedData.length >= 4 + length) { const messageBuffer = receivedData.slice(0, 4 + length); const message = decodeMCP(messageBuffer); console.log("Received message:", message); // 处理消息 if (message.type === "document_content") { console.log("Received document content:", message.data); } receivedData = receivedData.slice(4 + length); // 移除已处理的消息 } else { break; // 等待更多数据 } } }); socket.on("end", () => { console.log("Client disconnected."); }); socket.on("error", (err) => { console.error("Socket error:", err); }); }); server.listen(port, () => { console.log(`Server listening on port ${port}`); }); } // 客户端 function connectToServer(port, message) { const client = net.createConnection({ port: port }, () => { console.log("Connected to server."); const encodedMessage = encodeMCP(message.type, message.data); client.write(encodedMessage); }); client.on("data", (data) => { console.log("Received data from server:", data.toString()); client.end(); }); client.on("end", () => { console.log("Disconnected from server."); }); client.on("error", (err) => { console.error("Client error:", err); }); } // 示例用法 async function main() { const port = 8080; // 启动服务器 startServer(port); // 等待服务器启动 await new Promise(resolve => setTimeout(resolve, 1000)); // 读取 Word 文档内容 const documentContent = await readWordDocument("my-document.docx"); if (documentContent) { // 创建 MCP 消息 const message = { type: "document_content", data: documentContent, }; // 连接到服务器并发送消息 connectToServer(port, message); } } main(); ``` **解释:** * **`net` 模块:** Node.js 的网络模块，用于创建 TCP 服务器和客户端。 * **`encodeMCP` 函数:** 将消息编码为 MCP 协议格式。 它将消息类型和数据转换为 JSON 字符串，然后计算字符串的长度，并将长度作为 4 字节的大端整数添加到消息的前面。 * **`decodeMCP` 函数:** 将 MCP 协议格式的消息解码为 JavaScript 对象。 它首先读取消息长度，然后读取消息内容，并将其解析为 JSON 对象。 * **`startServer` 函数:** 启动一个 TCP 服务器，监听指定端口。 当客户端连接时，它会接收数据，解码 MCP 消息，并处理消息。 * **`connectToServer` 函数:** 连接到指定端口的 TCP 服务器，并发送 MCP 消息。 * **示例用法:** 首先启动服务器，然后读取 Word 文档的内容，创建一个包含文档内容的 MCP 消息，并将其发送到服务器。 **关键点:** * **MCP 协议定义:** 你需要根据实际需求定义 MCP 协议的格式。 上面的示例使用 JSON 格式，并添加了消息长度字段。 * **错误处理:** 在实际应用中，需要添加更完善的错误处理机制，例如处理网络连接错误，数据解析错误等。 * **数据分块:** 如果 Word 文档非常大，可能需要将文档内容分成多个块进行传输。 * **安全性:** 如果需要传输敏感数据，需要考虑使用加密技术，例如 TLS/SSL。 * **库的选择:** `docx` 和 `mammoth` 只是处理 Word 文档的其中两种库。 还有其他的库，例如 `officegen`，可以用于创建更复杂的 Word 文档。 选择合适的库取决于你的具体需求。 **总结:** 这个方案提供了一个基本的框架，用于在 Node.js 中实现 Word 文档的读取和写入，并使用 MCP 协议进行通信。 你需要根据实际需求调整代码，并添加必要的错误处理和安全性措施。 记住安装所需的 npm 包：`npm install docx mammoth`。 如果需要更复杂的 Word 文档处理功能，可以考虑使用其他的 Word 文档处理库。

DB Query MCP

Query SQLite databases via AI — safe read-only mode

advanced-seo-mcp

Provides AI agents with professional-grade SEO capabilities including on-page analysis, technical audits, PageSpeed insights, and Ahrefs data integration.

MCP Trading Server

Exposes Alpaca paper trading tools (get positions and account) via SSE transport, designed for Claude Desktop and n8n.

MCP Inventory

Enables CRUD and analytical operations on a SQLite inventory database, including product management and value calculations.

Useful-mcps

以下是一些实用的小型 MCP 服务器，包括： * docx\_replace：替换 Word 文档中的标签 * yt-dlp：基于章节提取章节和字幕 * mermaid：使用 mermaidchart.com API 生成和渲染图像

Fact Check MCP

Verifies claims with verdicts (supported/disputed/unverifiable), confidence scores, and cited sources by cross-referencing FoundryNet Data Network and web search.

Org-roam MCP Server

Enables interaction with org-roam knowledge bases, allowing search, retrieval, creation, and linking of notes while respecting org-roam's file structure and conventions.

FastMCP Document Analyzer

A comprehensive document analysis server that performs sentiment analysis, keyword extraction, readability scoring, and text statistics while providing document management capabilities including storage, search, and organization.

Pedalpalooza MCP Server

Fetches bike ride events from the Pedalpalooza calendar and allows LLMs to query rides by date, pace, difficulty, and family-friendliness.

legalize-mcp

A read-only MCP server providing access to national legislation from 37 jurisdictions via the legalize-dev corpus, with tools for searching, retrieving laws, and browsing reform history.

WeaveTab-MCP

The Zero-Setup Local Browser MCP. Enables AI agents to control web browsers via CDP with zero vision tokens and high-speed DOM mapping.

TunnelHub MCP

Connects MCP clients to TunnelHub to monitor automations, inspect executions, and analyze logs or traces. It enables users to manage environments and troubleshoot integration failures through natural language commands.

che-blender-mcp

Enables executing Python scripts, rendering scenes, and controlling 3D objects in Blender via Claude, with support for Cycles and EEVEE engines.

LINE Shopping API MCP

MCP Server for the LINE Shopping API, enabling AI agents and tools to interact with LINE Shopping data and operations via the Model Context Protocol.

MCP OpenDART

Enables AI assistants to access South Korea's financial disclosure system (OpenDART), allowing users to retrieve corporate financial reports, disclosure documents, shareholder information, and automatically extract and search financial statement notes through natural language queries.

Coconuts MCP Server

Enables management of Google Maps saved places through a SQLite database. Allows users to store, query, and organize their Google Maps places data locally.

Graphistry MCP

GPU-accelerated graph visualization and analytics server for Large Language Models that integrates with Model Control Protocol (MCP), enabling AI assistants to visualize and analyze complex network data.

mcp-codexreview

Provides MCP tools to review git changes using OpenAI Codex CLI, enabling code review of unstaged, staged, or last-commit changes.

mcp-claudinho

Claudinho gives any MCP client live 2026 World Cup scores, fixtures, group standings, read-only prediction-market signals (Polymarket, informational only), and ready-to-paste match cards. Key-free; the schedule is bundled offline — only live state hits ESPN. Independent fan project — not affiliated with FIFA or Anthropic.

HPE Aruba Networking Central MCP Server

Exposes 90 production-grade tools for interacting with the complete HPE Aruba Networking Central REST API surface, including network inventory, configuration, and security management. It features enterprise-ready OAuth2 handling and semantic tool filtering for optimized performance with both hosted and local LLMs.

Claude Team MCP Server

Enables Claude Code to orchestrate a team of independent Claude Code sessions within iTerm2 for parallel task execution and isolated git worktree management. It provides tools to spawn, monitor, and message worker sessions while maintaining full visibility and control over their terminal windows.

Remote MCP Server (Authless)

A deployable Cloudflare Workers service that implements Model Context Protocol without authentication, allowing AI models to access custom tools via clients like Claude Desktop or Cloudflare AI Playground.

Research Powerpack MCP

Enables AI assistants to perform comprehensive research by searching Google, mining Reddit discussions, scraping web content with JS rendering, and synthesizing findings with citations into structured context.

Jokes MCP Server

Enables fetching jokes (e.g., Chuck Norris jokes) via the Model Context Protocol, usable with Microsoft Copilot Studio and other MCP-compatible clients.

mcp-session-insight

Enables AI assistants to query and analyze past Claude Code sessions, providing structured insights like file changes, decisions, errors, and git history across projects.