发现优秀的 MCP 服务器

NoLang MCP Server

Enables MCP clients like Claude Desktop to generate AI-powered videos through the NoLang API, providing a standardized interface for video creation from text input.

Banana Accounting MCP Server

Exposes the Banana Accounting Plus Integrated Web Server API V2 to LLMs, enabling reading accounts, balances, journals, reports, and VAT data, and creating new accounting files from a local Banana Accounting instance.

Jokes MCP Server

Enables users to fetch jokes from various categories including Chuck Norris jokes and Dad jokes through a standardized MCP interface. Provides integration with Microsoft Copilot Studio and GitHub Copilot for AI-powered humor delivery.

Kokoro TTS MCP Server

Enables text-to-speech conversion using the Kokoro TTS model, running fully locally with native JavaScript. Supports multiple voices, speed control, and integrates with MCP-compatible clients.

MCP Gemini CLI Base

A base setup for creating MCP servers that integrate with Google's Gemini CLI, including example implementations for weather data retrieval from OpenWeatherMap and basic resource/tool definitions using FastMCP.

mcp-arcgis-abbotsford

Enables querying City of Abbotsford open geospatial data (parcels, zoning, public works) via ArcGIS Feature Services, with tools to search datasets, query layers, and retrieve schema info.

SchemaVault

MCP server for storing and retrieving database schema information for LLMs. Enables auto-loading Databricks Unity Catalog schemas and vector-based semantic search via configurable embedding service.

Curl MCP Server

A comprehensive HTTP/HTTPS tool server using curl, supporting various authentication methods, file uploads, downloads, and custom curl commands.

vibeops

Enables agents to read and drive a local-first Kanban board for issue tracking, allowing them to list, create, update, and resolve issues from Claude Code sessions.

security-mcp

Fetches GitHub Code Scanning alerts and applies minimal local patches to fix vulnerabilities, without creating branches, commits, or PRs.

health-export-mcp

An MCP server that enables AI agents to query Apple Health data (190+ metrics) in natural language, including trends, comparisons, and structured exports.

EventWhisper

Enables fast, scriptable access to Windows .evtx event logs for incident response and digital forensics. Supports filtering events by time windows, Event IDs, and keywords with field projection to reduce output size.

PDF Splitter MCP Server

Provides random access to PDF contents with selective page extraction, text search, outline navigation, image extraction, and page rendering capabilities. Reduces token usage by allowing targeted content extraction instead of processing entire documents.

github-talent-mcp

MCP server that searches, scores, and ranks GitHub developers for technical recruiting.

Haiguitang MCP Server

一个 MCP 服务器，允许用户游玩“海龟汤”谜题游戏，并由 LLM（大型语言模型）担任游戏主持人，提供访问游戏规则、谜题和全面谜题信息的工具。

putio-mcp-server

For interacting with put.io, an MCP (presumably meaning "Media Center Platform" or similar) server could be implemented in a few ways. Here are a few options, ranging from simple to more complex, along with considerations for each: **1. Simple Scripting with API Calls (Python, Bash, etc.)** * **Concept:** Use a scripting language like Python or Bash to directly interact with the put.io API. This is the most basic approach. * **How it Works:** * Use `curl`, `wget`, or a Python library like `requests` to make HTTP requests to the put.io API endpoints. * Authenticate using your put.io API key (obtained from your put.io account settings). * Implement functions to: * List files/folders. * Start downloads (by adding torrents or URLs). * Check download status. * Potentially stream files (if your MCP supports direct URL playback). * Expose these functions through a simple command-line interface or a very basic web interface. * **Pros:** * Simple to set up. * Good for basic automation. * Low resource usage. * **Cons:** * Requires scripting knowledge. * Limited features. * Not very user-friendly. * No built-in media management. * **Example (Python):** ```python import requests import json API_KEY = "YOUR_PUTIO_API_KEY" BASE_URL = "https://api.put.io/v2" def list_files(parent_id=0): url = f"{BASE_URL}/files/list?oauth_token={API_KEY}&parent_id={parent_id}" response = requests.get(url) response.raise_for_status() # Raise HTTPError for bad responses (4xx or 5xx) data = response.json() return data['files'] def add_transfer(url): url = f"{BASE_URL}/transfers/add?oauth_token={API_KEY}&url={url}" response = requests.post(url) response.raise_for_status() data = response.json() return data # Example usage files = list_files() for file in files: print(f"{file['name']} (ID: {file['id']})") # Add a transfer transfer_result = add_transfer("magnet:?xt=urn:btih:YOUR_MAGNET_LINK") print(f"Transfer added: {transfer_result}") ``` **2. Integration with Existing Media Server Software (Plex, Emby, Jellyfin)** * **Concept:** Use the API of an existing media server to manage and stream files downloaded by put.io. This is a more robust and user-friendly approach. * **How it Works:** * **Download Automation:** Use a script (like the one above, or a more sophisticated tool like Sonarr/Radarr) to automatically download files to a specific folder on your server. * **Media Server Configuration:** Configure Plex, Emby, or Jellyfin to monitor that folder. They will automatically scan and index the new files. * **Streaming:** Use the Plex/Emby/Jellyfin client apps to browse and stream your media. * **Pros:** * Excellent user experience. * Automatic media organization and metadata retrieval. * Streaming to a wide range of devices. * **Cons:** * Requires setting up and configuring a media server. * More resource intensive than a simple script. * Relies on the media server's API and functionality. * **Key Tools:** * **Plex:** Popular, proprietary media server. * **Emby:** Open-source media server with a premium option. * **Jellyfin:** Completely free and open-source media server. * **Sonarr/Radarr:** Tools for automating TV show and movie downloads. They can be configured to use put.io as a download client. **3. Custom Web Application (Flask, Django, Node.js)** * **Concept:** Build a custom web application that provides a user interface for interacting with the put.io API and managing your media. * **How it Works:** * **Backend:** Use a framework like Flask (Python), Django (Python), or Node.js (JavaScript) to create a web server. * **API Integration:** Use the put.io API to implement features like: * Browsing files. * Adding transfers. * Monitoring transfer status. * Deleting files. * **Frontend:** Create a user interface using HTML, CSS, and JavaScript (potentially with a framework like React, Vue.js, or Angular). * **Media Streaming:** Implement direct streaming from put.io (more complex) or rely on downloading files to a local directory and using a media server (like Plex) for streaming. * **Pros:** * Highly customizable. * Can integrate with other services. * Can provide a unique user experience. * **Cons:** * Requires significant development effort. * More complex to set up and maintain. * **Example (Conceptual Flask):** ```python from flask import Flask, render_template, request, redirect, url_for import requests import json app = Flask(__name__) API_KEY = "YOUR_PUTIO_API_KEY" BASE_URL = "https://api.put.io/v2" def list_files(parent_id=0): # ... (same as before) ... def add_transfer(url): # ... (same as before) ... @app.route("/") def index(): files = list_files() return render_template("index.html", files=files) @app.route("/add_transfer", methods=["POST"]) def add_transfer_route(): url = request.form["url"] add_transfer(url) return redirect(url_for("index")) if __name__ == "__main__": app.run(debug=True) ``` **Choosing the Right Approach:** * **Simple Scripting:** Best for basic automation and command-line users. * **Media Server Integration:** Best for a seamless media streaming experience. * **Custom Web Application:** Best for maximum flexibility and control, but requires the most development effort. **Important Considerations:** * **API Key Security:** Never hardcode your API key directly into your code if you plan to share it. Use environment variables or a configuration file. * **Error Handling:** Implement robust error handling to gracefully handle API errors and network issues. * **Rate Limiting:** Be aware of put.io's API rate limits and implement appropriate throttling in your code. * **Authentication:** For a web application, implement proper user authentication and authorization. * **Storage:** Consider where you will store downloaded files if you are not streaming directly from put.io. * **Legal:** Ensure you are using put.io and downloading content legally. **Translation to Chinese:** Here's a translation of the above information into Chinese: **与 put.io 交互的 MCP 服务器** 为了与 put.io 交互，可以采用几种方法来实现 MCP（可能意味着“媒体中心平台”或类似的东西）服务器。 以下是一些选项，从简单到复杂，以及每个选项的注意事项： **1. 简单的脚本编写与 API 调用 (Python, Bash 等)** * **概念:** 使用像 Python 或 Bash 这样的脚本语言直接与 put.io API 交互。 这是最基本的方法。 * **工作原理:** * 使用 `curl`、`wget` 或像 `requests` 这样的 Python 库向 put.io API 端点发出 HTTP 请求。 * 使用您的 put.io API 密钥进行身份验证（从您的 put.io 帐户设置中获取）。 * 实现以下功能的函数： * 列出文件/文件夹。 * 启动下载（通过添加种子或 URL）。 * 检查下载状态。 * 可能流式传输文件（如果您的 MCP 支持直接 URL 播放）。 * 通过简单的命令行界面或非常基本的 Web 界面公开这些功能。 * **优点:** * 设置简单。 * 适用于基本自动化。 * 资源占用低。 * **缺点:** * 需要脚本编写知识。 * 功能有限。 * 不是很用户友好。 * 没有内置的媒体管理。 * **示例 (Python):** ```python import requests import json API_KEY = "YOUR_PUTIO_API_KEY" BASE_URL = "https://api.put.io/v2" def list_files(parent_id=0): url = f"{BASE_URL}/files/list?oauth_token={API_KEY}&parent_id={parent_id}" response = requests.get(url) response.raise_for_status() # Raise HTTPError for bad responses (4xx or 5xx) data = response.json() return data['files'] def add_transfer(url): url = f"{BASE_URL}/transfers/add?oauth_token={API_KEY}&url={url}" response = requests.post(url) response.raise_for_status() data = response.json() return data # 示例用法 files = list_files() for file in files: print(f"{file['name']} (ID: {file['id']})") # 添加一个传输 transfer_result = add_transfer("magnet:?xt=urn:btih:YOUR_MAGNET_LINK") print(f"传输已添加: {transfer_result}") ``` **2. 与现有媒体服务器软件集成 (Plex, Emby, Jellyfin)** * **概念:** 使用现有媒体服务器的 API 来管理和流式传输 put.io 下载的文件。 这是一种更强大且用户友好的方法。 * **工作原理:** * **下载自动化:** 使用脚本（如上面的脚本，或更复杂的工具，如 Sonarr/Radarr）自动将文件下载到服务器上的特定文件夹。 * **媒体服务器配置:** 配置 Plex、Emby 或 Jellyfin 来监视该文件夹。 它们将自动扫描和索引新文件。 * **流式传输:** 使用 Plex/Emby/Jellyfin 客户端应用程序浏览和流式传输您的媒体。 * **优点:** * 出色的用户体验。 * 自动媒体组织和元数据检索。 * 流式传输到各种设备。 * **缺点:** * 需要设置和配置媒体服务器。 * 比简单的脚本更消耗资源。 * 依赖于媒体服务器的 API 和功能。 * **关键工具:** * **Plex:** 流行的专有媒体服务器。 * **Emby:** 具有高级选项的开源媒体服务器。 * **Jellyfin:** 完全免费和开源的媒体服务器。 * **Sonarr/Radarr:** 用于自动化电视节目和电影下载的工具。 可以配置它们以使用 put.io 作为下载客户端。 **3. 自定义 Web 应用程序 (Flask, Django, Node.js)** * **概念:** 构建一个自定义 Web 应用程序，该应用程序提供一个用户界面，用于与 put.io API 交互并管理您的媒体。 * **工作原理:** * **后端:** 使用像 Flask (Python)、Django (Python) 或 Node.js (JavaScript) 这样的框架来创建 Web 服务器。 * **API 集成:** 使用 put.io API 来实现以下功能： * 浏览文件。 * 添加传输。 * 监控传输状态。 * 删除文件。 * **前端:** 使用 HTML、CSS 和 JavaScript（可能使用像 React、Vue.js 或 Angular 这样的框架）创建一个用户界面。 * **媒体流式传输:** 实现从 put.io 的直接流式传输（更复杂）或依赖于将文件下载到本地目录并使用媒体服务器（如 Plex）进行流式传输。 * **优点:** * 高度可定制。 * 可以与其他服务集成。 * 可以提供独特的用户体验。 * **缺点:** * 需要大量的开发工作。 * 设置和维护更复杂。 * **示例 (概念性 Flask):** ```python from flask import Flask, render_template, request, redirect, url_for import requests import json app = Flask(__name__) API_KEY = "YOUR_PUTIO_API_KEY" BASE_URL = "https://api.put.io/v2" def list_files(parent_id=0): # ... (与之前相同) ... def add_transfer(url): # ... (与之前相同) ... @app.route("/") def index(): files = list_files() return render_template("index.html", files=files) @app.route("/add_transfer", methods=["POST"]) def add_transfer_route(): url = request.form["url"] add_transfer(url) return redirect(url_for("index")) if __name__ == "__main__": app.run(debug=True) ``` **选择正确的方法:** * **简单脚本编写:** 最适合基本自动化和命令行用户。 * **媒体服务器集成:** 最适合无缝的媒体流式传输体验。 * **自定义 Web 应用程序:** 最适合最大的灵活性和控制，但需要最多的开发工作。 **重要注意事项:** * **API 密钥安全:** 如果您计划共享您的代码，切勿将您的 API 密钥直接硬编码到您的代码中。 使用环境变量或配置文件。 * **错误处理:** 实施强大的错误处理，以优雅地处理 API 错误和网络问题。 * **速率限制:** 请注意 put.io 的 API 速率限制，并在您的代码中实施适当的限制。 * **身份验证:** 对于 Web 应用程序，实施适当的用户身份验证和授权。 * **存储:** 如果您不直接从 put.io 流式传输，请考虑将下载的文件存储在哪里。 * **法律:** 确保您合法地使用 put.io 并下载内容。 This translation should be helpful. Remember to replace `"YOUR_PUTIO_API_KEY"` and `"magnet:?xt=urn:btih:YOUR_MAGNET_LINK"` with your actual API key and magnet link. Good luck!

MarketIntel MCP Server

Enables AI assistants to perform real-time market research including competitor analysis, pricing intelligence, and company overviews via live web search through Tavily API.

Windows-MCP

A lightweight server that enables AI agents to interact natively with the Windows operating system for tasks like UI automation and application control. It allows LLMs to perform file navigation, simulate user input, and manage windows without requiring specialized computer vision models.

a2a-bridge

Enables bidirectional A2A communication between AI coding agents, allowing Claude Code to reach out to and be reached by other A2A agents via an MCP bridge and an HTTP server.

BitBadges Builder MCP Server

MCP server for building, auditing, and querying BitBadges collections via AI. Works with Claude Desktop and other MCP clients to generate transaction JSON for BitBadges tokens and collections (deprecated, use bitbadgesjs-sdk).

agent-cold-email

Agent-native cold-email infrastructure providing ~12 high-level tools for domain setup, mailbox provisioning, campaign launch, inbox management, and metrics, all operated via MCP or CLI.

Enterprise Rent-A-Car MCP Connector

Enables AI assistants to search for vehicles, manage reservations, and access Enterprise Plus loyalty account information through headless browser automation on enterprise.com. It supports the complete car rental workflow including vehicle searching, booking, modifications, and points balance checks.

Memory Bank MCP Server

Provides a structured documentation system for context preservation in AI assistant environments, helping users create and manage memory banks for their projects.

Newsnow MCP Server

Enables access to news from multiple sources including Google News and DuckDuckGo, supporting queries by location, category, site, date range, and providing top news headlines with detailed information.

Remote MCP Server (Authless)

A template for deploying an unauthenticated Model Context Protocol server on Cloudflare Workers that can be accessed remotely by MCP clients like Claude Desktop or the Cloudflare AI Playground.

freshrss-mcp

Enables AI assistants to read and manage FreshRSS feeds, including listing subscriptions, browsing articles, marking read/unread, and starring.

swiss-courts-mcp

MCP server for searching Swiss court decisions from federal and cantonal courts via entscheidsuche.ch. Enables full-text search, law reference lookup, and filtering by canton, court level, and date without API keys.

VERDICT

Enables autonomous DFIR investigation by turning the SIFT toolchain into evidence-safe MCP functions, with self-correction, corroboration, and traceable audit trails.

trading-skills

Provides 32 trading analysis tools for AI-powered market analysis, including real-time data, technical indicators, options Greeks, scanners, and Interactive Brokers portfolio management, all accessible via natural language in Claude Desktop.