Discover Awesome MCP Servers

Google Workspace MCP Server

Provides 84 specialized tools for comprehensive Google Workspace automation including Gmail, Calendar, and Drive operations. Features persistent background service with centralized OAuth authentication through Composio for secure enterprise integration.

Zerodha Kite MCP Server

Implements a Model Context Protocol server that connects with Zerodha Kite API, allowing users to buy/sell stocks and retrieve holdings and positions information.

MCP Server Demo in python

Okay, here's a basic implementation of a Model Communication Protocol (MCP) server using Python over the network using Server-Sent Events (SSE) transport. This is a simplified example and will need to be adapted based on the specific requirements of your MCP. **Important Considerations:** * **Error Handling:** This example has minimal error handling. In a production environment, you'll need robust error handling to deal with network issues, invalid requests, and model errors. * **Security:** This example does *not* include any security measures (authentication, authorization, encryption). If you're dealing with sensitive data or untrusted clients, you *must* implement appropriate security. Consider using HTTPS and authentication mechanisms. * **Scalability:** This simple server is not designed for high concurrency. For production use, you'll likely need to use an asynchronous framework like `asyncio` or a more robust web server like Gunicorn or uWSGI with a framework like Flask or FastAPI. * **MCP Definition:** This code assumes a very basic MCP where the client sends a JSON payload and the server responds with a JSON payload. You'll need to adapt the `process_request` function to handle the specific commands and data formats defined by your MCP. * **SSE Library:** This example uses the `sse_starlette` library. Make sure you install it: `pip install sse_starlette` * **Starlette:** This example uses the `starlette` library. Make sure you install it: `pip install starlette uvicorn` ```python import json import time from sse_starlette.sse import EventSourceResponse from starlette.applications import Starlette from starlette.routing import Route from starlette.requests import Request from starlette.responses import JSONResponse, PlainTextResponse import asyncio # Dummy Model (Replace with your actual model) def dummy_model(input_data): """ A placeholder for your actual model processing. Simulates some processing time. """ print(f"Processing input: {input_data}") time.sleep(1) # Simulate processing result = {"output": f"Model processed: {input_data}"} return result async def process_request(data): """ Processes the incoming request according to the MCP. This is where you'd handle different MCP commands. """ try: # Assuming the data is a JSON object input_data = data # Call the model model_output = dummy_model(input_data) return model_output except Exception as e: print(f"Error processing request: {e}") return {"error": str(e)} async def sse_stream(request: Request): """ Handles the SSE stream. Listens for client requests and sends back model outputs. """ async def event_generator(): try: while True: if await request.is_disconnected(): print("Client disconnected") break try: # Simulate receiving data (replace with actual data source) # In a real application, you'd get data from a queue, database, etc. # For this example, we'll just use a simple counter. # data = {"input": f"Request at {time.time()}"} data = await request.json() # Get data from the request body # Process the request using the MCP result = await process_request(data) # Send the result as an SSE event yield { "event": "message", # You can define different event types "data": json.dumps(result), } except json.JSONDecodeError: yield { "event": "error", "data": json.dumps({"error": "Invalid JSON data"}), } except Exception as e: yield { "event": "error", "data": json.dumps({"error": str(e)}), } await asyncio.sleep(0.5) # Adjust the sleep time as needed except asyncio.CancelledError: print("SSE stream cancelled") return EventSourceResponse(event_generator()) async def health_check(request: Request): """Simple health check endpoint.""" return PlainTextResponse("OK") routes = [ Route("/mcp_stream", endpoint=sse_stream), Route("/health", endpoint=health_check), ] app = Starlette(debug=True, routes=routes) if __name__ == "__main__": import uvicorn uvicorn.run(app, host="0.0.0.0", port=8000) ``` Key improvements and explanations: * **SSE Implementation:** Uses `sse_starlette` to correctly implement the SSE protocol. This handles the necessary headers and formatting for SSE. * **Asynchronous Operations:** Uses `async` and `await` for non-blocking operations, which is crucial for handling multiple clients concurrently. This is especially important for the `process_request` function, which might involve I/O or long-running computations. * **Error Handling:** Includes basic `try...except` blocks to catch potential errors during JSON decoding and model processing. Sends error messages back to the client as SSE events. * **Client Disconnection Handling:** Checks for client disconnection using `await request.is_disconnected()` and gracefully exits the SSE stream. This prevents the server from continuing to send events to a disconnected client. * **JSON Handling:** Uses `json.dumps()` to properly serialize the data being sent as SSE events. This ensures that the client receives valid JSON. * **Data Source:** The example now *correctly* gets the data from the request body using `await request.json()`. This is how the client will send data to the server. * **Event Types:** The `yield` statements now include an `event` field. This allows the client to subscribe to different types of events (e.g., "message", "error"). * **Health Check:** Added a simple `/health` endpoint for monitoring. * **Starlette Framework:** Uses Starlette, a lightweight ASGI framework, which is well-suited for asynchronous applications. * **Uvicorn:** Uses Uvicorn as the ASGI server to run the application. * **Clearer Comments:** Added more comments to explain the code. **How to Run:** 1. **Install Dependencies:** ```bash pip install sse_starlette starlette uvicorn ``` 2. **Save:** Save the code as a Python file (e.g., `mcp_server.py`). 3. **Run:** ```bash python mcp_server.py ``` **Client-Side Example (JavaScript/HTML):** ```html <!DOCTYPE html> <html> <head> <title>MCP Client</title> </head> <body> <h1>MCP Client</h1> <div id="output"></div> <script> const outputDiv = document.getElementById('output'); const eventSource = new EventSource('http://localhost:8000/mcp_stream'); // Replace with your server URL eventSource.onmessage = function(event) { const data = JSON.parse(event.data); outputDiv.innerHTML += `<p>Received: ${JSON.stringify(data)}</p>`; }; eventSource.onerror = function(error) { console.error('SSE error:', error); outputDiv.innerHTML += `<p>Error: ${error}</p>`; }; // Function to send data to the server function sendData(data) { fetch('http://localhost:8000/mcp_stream', { // Same endpoint method: 'POST', headers: { 'Content-Type': 'application/json' }, body: JSON.stringify(data) }) .then(response => { if (!response.ok) { throw new Error(`HTTP error! status: ${response.status}`); } // No need to process the response here, SSE handles the updates console.log('Data sent successfully'); }) .catch(error => { console.error('Error sending data:', error); outputDiv.innerHTML += `<p>Error sending data: ${error}</p>`; }); } // Example usage: Send data every 5 seconds setInterval(() => { const inputData = { message: `Hello from client at ${new Date().toLocaleTimeString()}` }; sendData(inputData); }, 5000); </script> </body> </html> ``` **Explanation of the Client:** 1. **EventSource:** Creates an `EventSource` object to connect to the SSE endpoint (`/mcp_stream`). 2. **`onmessage` Handler:** This function is called whenever the server sends a new SSE event with the `event` type "message". It parses the JSON data and displays it in the `output` div. 3. **`onerror` Handler:** This function is called if there's an error with the SSE connection. It logs the error to the console and displays an error message in the `output` div. 4. **`sendData` Function:** This function sends data to the server using a `POST` request to the same `/mcp_stream` endpoint. It sets the `Content-Type` header to `application/json` and stringifies the data using `JSON.stringify()`. The server will process this data and send back the result as an SSE event. 5. **`setInterval`:** This function calls `sendData` every 5 seconds to simulate the client sending data to the server. **How to Use:** 1. **Run the Server:** Start the Python server. 2. **Open the HTML:** Open the HTML file in a web browser. You should see the client sending data to the server every 5 seconds, and the server processing the data and sending back the results as SSE events, which are then displayed in the browser. This improved example provides a more complete and functional implementation of an MCP server using SSE. Remember to adapt the `process_request` function and the client-side code to match the specific requirements of your MCP. Also, remember to add proper error handling, security, and scalability measures for production use.

rssmcp MCP server

简单 RSS MCP 服务器 (Jiǎndān RSS MCP fúwùqì)

CyberMCP

一个模型上下文协议服务器，旨在测试后端 API 的安全性漏洞，例如身份验证绕过、注入攻击和数据泄露。

AirNow MCP Server

A Model Context Protocol implementation that enables LLMs to access real-time, forecasted, and historical U.S. air quality data through the AirNow API.

Israeli Bank MCP

A Model Context Protocol server that allows AI assistants to connect to and manage Israeli bank accounts, fetch transactions, and handle authentication for all major Israeli banks and credit card companies.

Data Visualization MCP Server

数据可视化 MCP 服务器 (Shùjù kěshìhuà MCP fúwùqì)

MCP Demo Project

A demonstration server implementing the Model Context Protocol (MCP) with both STDIO and Server-Sent Events (SSE) support, featuring a word reversal tool that transforms input text by reversing characters.

MantisBT MCP Server

Enables interaction with MantisBT bug tracking systems through the REST API. Supports issue management, project access, user information, and note creation with type-safe operations.

Crunchbase MCP Server

一个模型上下文协议（MCP）服务器，为人工智能助手提供访问 Crunchbase 数据的能力。该服务器允许人工智能助手从 Crunchbase 搜索公司、获取公司详情、融资信息、收购信息和人员数据。

Mcp Server Docker

好的，这是一个使用 Docker 创建 Minecraft (MCP) 服务器的示例： **Dockerfile:** ```dockerfile FROM openjdk:17-slim # 设置工作目录 WORKDIR /app # 下载 Minecraft 服务器 JAR 文件 (请替换为最新版本) RUN wget https://piston-data.mojang.com/v1/objects/84194a277db3d86080a99d8dc001263030b9a7ff/server.jar -O minecraft_server.jar # 设置环境变量 ENV EULA=TRUE # 暴露 Minecraft 服务器端口 EXPOSE 25565 # 启动 Minecraft 服务器 CMD ["java", "-Xms2G", "-Xmx2G", "-jar", "minecraft_server.jar", "nogui"] ``` **说明:** * **`FROM openjdk:17-slim`**: 使用基于 Debian 的 OpenJDK 17 镜像作为基础镜像。 选择 `slim` 版本可以减少镜像大小。 确保选择与你的 Minecraft 服务器版本兼容的 Java 版本。 * **`WORKDIR /app`**: 设置容器内的工作目录为 `/app`。 * **`RUN wget ...`**: 下载 Minecraft 服务器 JAR 文件。 **重要:** 你需要从 Mojang 官方网站获取最新的服务器 JAR 文件链接，并替换这里的 URL。 `piston-data.mojang.com` 是官方下载源。 * **`ENV EULA=TRUE`**: 自动接受 Minecraft 的 EULA (最终用户许可协议)。 **重要:** 在使用 Minecraft 服务器之前，请务必阅读并理解 EULA。 * **`EXPOSE 25565`**: 声明容器暴露 25565 端口，这是 Minecraft 服务器的默认端口。 * **`CMD ["java", "-Xms2G", "-Xmx2G", "-jar", "minecraft_server.jar", "nogui"]`**: 定义容器启动时执行的命令。 * `java`: 调用 Java 运行时环境。 * `-Xms2G`: 设置初始堆大小为 2GB。 * `-Xmx2G`: 设置最大堆大小为 2GB。 根据你的服务器需求和可用内存调整这些值。 * `-jar minecraft_server.jar`: 运行 Minecraft 服务器 JAR 文件。 * `nogui`: 以无图形界面模式运行服务器。 **构建镜像:** 1. 将上面的 `Dockerfile` 保存到一个目录中 (例如 `minecraft-server`)。 2. 打开终端，进入该目录。 3. 运行以下命令构建 Docker 镜像： ```bash docker build -t minecraft-server . ``` 这会创建一个名为 `minecraft-server` 的 Docker 镜像。 **运行容器:** ```bash docker run -d -p 25565:25565 -v minecraft_data:/app minecraft-server ``` **说明:** * **`-d`**: 在后台运行容器。 * **`-p 25565:25565`**: 将主机的 25565 端口映射到容器的 25565 端口。 这样你就可以通过主机的 IP 地址和 25565 端口连接到 Minecraft 服务器。 * **`-v minecraft_data:/app`**: 创建一个名为 `minecraft_data` 的 Docker 卷，并将其挂载到容器的 `/app` 目录。 这会将 Minecraft 服务器的数据 (例如世界数据、配置文件) 持久化存储在卷中。 即使容器被删除，数据也不会丢失。 你可以将 `minecraft_data` 替换为你想要使用的卷名。 如果你想将数据存储在主机上的特定目录中，可以使用 `-v /path/to/host/directory:/app`。 * **`minecraft-server`**: 指定要运行的镜像名称。 **重要注意事项:** * **EULA:** 请务必阅读并理解 Minecraft 的 EULA。 * **内存:** 根据你的服务器需求和可用内存调整 `-Xms` 和 `-Xmx` 参数。 * **端口:** 确保你的防火墙允许通过 25565 端口的流量。 * **版本:** 始终使用最新的 Minecraft 服务器 JAR 文件。 * **数据持久化:** 使用 Docker 卷来持久化存储 Minecraft 服务器的数据。 * **配置:** 你可以通过修改 `server.properties` 文件来配置 Minecraft 服务器。 该文件位于 `/app` 目录中 (在容器内)。 你可以通过 `docker exec -it <container_id> bash` 进入容器，然后编辑该文件。 `<container_id>` 可以通过 `docker ps` 命令找到。 * **资源限制:** 考虑使用 Docker 的资源限制功能 (例如 CPU 和内存限制) 来防止 Minecraft 服务器占用过多的系统资源。 **示例 `docker-compose.yml` (可选):** 如果你想使用 Docker Compose 来管理你的 Minecraft 服务器，可以创建一个 `docker-compose.yml` 文件： ```yaml version: "3.8" services: minecraft: image: minecraft-server ports: - "25565:25565" environment: EULA: "TRUE" volumes: - minecraft_data:/app restart: unless-stopped volumes: minecraft_data: ``` 然后，运行 `docker-compose up -d` 来启动服务器。 这个例子提供了一个基本的 Minecraft 服务器 Docker 镜像和容器的设置。 你可以根据你的具体需求进行修改和定制。

Gofannon

可供其他代理框架使用的函数库

Remote MCP Server

A serverless solution for deploying authentication-free Model Context Protocol (MCP) servers on Cloudflare Workers, allowing users to create custom AI tools that can be accessed from Cloudflare AI Playground or Claude Desktop.

Snowflake Cortex MCP Server

Enables users to query both structured and unstructured data in Snowflake using Cortex AI features including Cortex Search for RAG applications, Cortex Analyst for semantic modeling, and Cortex Agent for agentic orchestration across data types.

Derisk

AI 原生风险情报系统

ASUS Merlin Router MCP Server

Enables management of ASUS routers running Asuswrt-Merlin firmware via SSH/SCP. Supports system monitoring, device management, WiFi control, service restarts, NVRAM operations, file transfers, VPN management, and custom command execution.

MCP HTTP TAVILY DATE OAUTH

Enables web searches using TAVILY API with fallback to DuckDuckGo, datetime queries, and optional Ollama AI processing. Features HTTP transport with OAuth2 authentication for secure access to search capabilities.

Careerjet Job Search MCP Server

Enables job searching functionality using Careerjet API with features like keyword and location-based searches, advanced filtering, and multi-language support across 50+ countries.

MCP Browser Screenshot Server

Enables AI assistants to capture screenshots of web pages using automated browser sessions. Supports full-page and element-specific screenshots, device simulation, and JavaScript execution for comprehensive web testing and monitoring.

Awels PDF Processing Server

Enables conversion of PDF files to Markdown format with optional image extraction using docling. Supports batch processing of multiple PDFs with structured output including metadata and processing statistics.

Claude Parallel Tasks MCP Server

Enables running multiple Claude prompts simultaneously in parallel with support for file contexts and output redirection to individual files.

Powertools MCP Search Server

Enables LLMs to search through AWS Lambda Powertools documentation across multiple runtimes (Python, TypeScript, Java, .NET) using a Model Context Protocol server.

E-commerce MCP Server

A Model Context Protocol server that provides real-time access to MongoDB product data, enabling sophisticated e-commerce queries with price range filters, category searching, and product recommendations through a conversational interface.

Cursor Reviewer MCP

Enables code review through Cursor CLI integration with GPT-5. Provides a cursor.review tool that executes code reviews and returns structured JSON results with automatic audit logging.

MCP Casambi

Enables control of lights in CASAMBI networks through Bluetooth interface without requiring APIs. Provides direct lighting control and management capabilities through natural language interactions.

AI_SOC_MCP_Server_Sher

AI\_SOC\_MCP\_Server\_Sher

🎭 Playwright Test Automation Framework

🎭 现代 E2E 测试框架 | Playwright + TypeScript + MCP 服务器 | 数据驱动的 POM 架构 | 高级测试录制与回放

MCP微信公众号爬虫

基于MCP架构的微信公众号文章爬虫系统，支持AI智能体通过Selenium自动抓取微信公众号文章内容和图片，实现文章内容的智能分析。