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Mcp Android Adb Server

Mcp Android Adb Server

Using AI Large Language Models to Operate Android Devices.

putio-mcp-server

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!

Google Ads Content Generator MCP Server

Google Ads Content Generator MCP Server

An MCP server implementation that enables generation of Google Ads content through Claude Code, Claude Desktop, and other MCP-compatible clients.

chainlink-sentinel

chainlink-sentinel

MCP server for Chainlink ecosystem monitoring with 6 tools: price feed tracking, VRF monitoring, automation status, CCIP tracking, and staking analytics. MIT licensed.

GraphQL MCP Server by CData

GraphQL MCP Server by CData

GraphQL MCP Server by CData

Todoist MCP Server

Todoist MCP Server

Connects AI assistants to Todoist for comprehensive task management, enabling natural language creation, updating, and organization of tasks, projects, sections, and labels.

Hatchet MCP Server

Hatchet MCP Server

Enables monitoring and debugging of Hatchet workflows and jobs through MCP clients like Claude Code. Users can list workflows, track run statuses, query queue metrics, and retrieve job results using natural language commands.

Appmixer MCP

Appmixer MCP

Enables LLMs to automate workflows and interact with SaaS products and APIs through Appmixer's integration platform. It provides tools for managing automation flows and dynamically exposes custom tools via an MCP Gateway.

EventWhisper

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.

Twitter MCP Server

Twitter MCP Server

Enables interaction with Twitter/X data to retrieve user profiles, search tweets, and track engagement metrics. It provides advanced capabilities for monitoring follower events, KOL activity, and accessing deleted tweets.

Chess.com MCP Server

Chess.com MCP Server

Provides tools to interact with the Chess.com Public API for fetching real-time player profiles and detailed game statistics. It enables LLMs to access information like player ratings, win/loss records, and current online status.

lawmem-ai/lawmem-mcp

lawmem-ai/lawmem-mcp

Persistent semantic memory-as-a-service for legal AI agents. Store and recall case notes, client context, and matter history via MCP. Namespace-isolated, audit-logged, and GDPR-compliant.

WeatherXM PRO MCP Server

WeatherXM PRO MCP Server

Enables access to WeatherXM PRO weather station data, including real-time observations, historical data, and hyperlocal forecasts through station queries, geographic searches, and H3 cell-based weather information.

Qinglong MCP Server

Qinglong MCP Server

Enables interaction with Qinglong Panel (青龙面板) to query, execute, and monitor scheduled tasks and subscriptions, including viewing task lists, running tasks synchronously or asynchronously, retrieving execution logs and status.

MCP PDF to Markdown Converter

MCP PDF to Markdown Converter

A multi-server system that converts PDF documents to Markdown format using FastMCP architecture with upload and convert servers orchestrated by a reactive client agent.

X(Twitter) MCP Server

X(Twitter) MCP Server

Enables creating, managing, and publishing X/Twitter posts, threads, and replies directly through Claude chat. Supports draft management with the ability to create, list, publish, and delete tweet drafts.

GlitchTip MCP Server

GlitchTip MCP Server

Enables AI assistants to query, analyze, and resolve errors within the GlitchTip error tracking platform by providing access to issue details and stacktraces. It allows users to list unresolved issues and mark them as fixed using natural language commands.

WooCommerce MCP Server

WooCommerce MCP Server

A production-grade MCP server for the WooCommerce REST API, enabling AI assistants to manage products, orders, customers, and store settings.

ChatGPT Escalation MCP Server

ChatGPT Escalation MCP Server

Enables autonomous coding agents to escalate complex technical questions to ChatGPT Desktop app via ToS-compliant UI automation, returning expert responses automatically without user intervention.

mcp-victoriametrics

mcp-victoriametrics

mcp-victoriametrics

Android Screenshot MCP Server

Android Screenshot MCP Server

Enables capturing screenshots from Android devices over WiFi for UI debugging and visual inspection during app development.

Remote MCP Server

Remote MCP Server

A Cloudflare Workers-based server for deploying Model Context Protocol tools without authentication, allowing integration with AI assistants like Claude Desktop and the Cloudflare AI Playground.

test

test

test

chrome-debug-mcp

chrome-debug-mcp

chrome-debug-mcp is an asynchronous Rust-based Model Context Protocol (MCP) server that allows AI agents and Large Language Models to natively debug Chromium-based browsers via the Chrome DevTools Protocol (CDP).

HubSpot MCP Server by CData

HubSpot MCP Server by CData

HubSpot MCP Server by CData

Web Search MCP

Web Search MCP

Enables web searching via DuckDuckGo and extracting readable content from any URL using Mozilla Readability, providing web context similar to Cursor's built-in functionality.

PostgreSQL MCP Server

PostgreSQL MCP Server

A Model Context Protocol server providing dual transport (HTTP and Stdio) access to PostgreSQL databases, allowing AI assistants to query databases and fetch schema information through natural language.

Telegram MCP Server

Telegram MCP Server

A Model Context Protocol server that enables Claude to interact with Telegram channels and groups through both direct API access and web scraping methods.

XMZ MCP Server

XMZ MCP Server

Enables AI models to interact with Tencent Cloud COS for cloud storage operations (upload, download, list files) and advanced media processing capabilities (image super-resolution, QR code recognition, video thumbnails, document conversion, and natural language-based file search).

Gahmen MCP Server

Gahmen MCP Server

Provides access to Singapore's data.gov.sg government datasets and collections, enabling search, metadata retrieval, and dataset downloads through the CKAN datastore API.