Discover Awesome MCP Servers

MCP TabNews Integration

A Model Context Protocol server that enables AI tools to interact with TabNews, providing capabilities to fetch content, comments, analytics, and RSS feeds through natural language.

Moling

MoLing 是一个基于电脑和浏览器使用的 MCP 服务器。它是一个本地部署、无依赖的办公 AI 助手。

Filesystem MCP Server

Enables comprehensive filesystem operations including reading/writing files, directory management, file searching, editing with diff preview, compression, hashing, and merging with dynamic directory access control.

Cosmos DB MCP Server by CData

Cosmos DB MCP Server by CData

aiohttp-mcp

构建在 aiohttp 之上的模型上下文协议 (MCP) 服务器的工具： Here are some tools and libraries that can help you build Model Context Protocol (MCP) servers on top of aiohttp: * **aiohttp:** This is the fundamental asynchronous HTTP server and client library for Python. You'll use it to handle incoming MCP requests and send responses. You'll need to understand how to define routes, handle requests, and serialize/deserialize data. * **asyncio:** Since aiohttp is built on asyncio, you'll need a good understanding of asynchronous programming concepts like event loops, coroutines, and tasks. This is crucial for handling concurrent requests efficiently. * **Marshmallow (or similar serialization library):** MCP often involves structured data. Marshmallow is a popular library for serializing and deserializing Python objects to and from formats like JSON. This helps you validate incoming requests and format outgoing responses according to the MCP specification. Alternatives include `attrs` with `cattrs`, or `pydantic`. * **JSON Schema (and a validator):** MCP implementations often use JSON Schema to define the structure and validation rules for the request and response payloads. Libraries like `jsonschema` can be used to validate incoming requests against a schema, ensuring that they conform to the MCP specification. * **gRPC (optional, but relevant for comparison):** While you're building on aiohttp, it's worth understanding gRPC. gRPC is a high-performance RPC framework that's often used for similar purposes as MCP. Understanding gRPC can help you make informed design decisions about your MCP implementation. If performance is critical, consider whether gRPC might be a better fit than a custom aiohttp-based solution. * **Logging:** Use Python's built-in `logging` module to log requests, errors, and other relevant information. This is essential for debugging and monitoring your MCP server. * **Testing Framework (pytest, unittest):** Write unit tests and integration tests to ensure that your MCP server is working correctly. `pytest` is a popular and flexible testing framework. * **OpenAPI/Swagger (optional):** If you want to document your MCP API, you can use OpenAPI (formerly Swagger). Tools like `aiohttp-apispec` can help you generate OpenAPI specifications from your aiohttp routes. This makes it easier for clients to understand and use your MCP server. **Example (Conceptual):** ```python import asyncio import json from aiohttp import web import marshmallow import jsonschema # Define your data models using Marshmallow class MyRequestSchema(marshmallow.Schema): input_data = marshmallow.fields.String(required=True) class MyResponseSchema(marshmallow.Schema): output_data = marshmallow.fields.String(required=True) # Define your JSON Schema (alternative to Marshmallow for validation) request_schema = { "type": "object", "properties": { "input_data": {"type": "string"} }, "required": ["input_data"] } async def handle_mcp_request(request): try: data = await request.json() # Option 1: Validate with JSON Schema try: jsonschema.validate(instance=data, schema=request_schema) except jsonschema.exceptions.ValidationError as e: return web.json_response({"error": str(e)}, status=400) # Option 2: Validate and deserialize with Marshmallow # try: # validated_data = MyRequestSchema().load(data) # except marshmallow.exceptions.ValidationError as err: # return web.json_response({"errors": err.messages}, status=400) # Process the request (replace with your actual logic) input_data = data['input_data'] # or validated_data['input_data'] output_data = f"Processed: {input_data}" # Serialize the response with Marshmallow response_data = MyResponseSchema().dump({"output_data": output_data}) return web.json_response(response_data) except Exception as e: print(f"Error: {e}") return web.json_response({"error": "Internal Server Error"}, status=500) async def main(): app = web.Application() app.add_routes([web.post('/mcp', handle_mcp_request)]) runner = web.AppRunner(app) await runner.setup() site = web.TCPSite(runner, 'localhost', 8080) await site.start() print("Server started on http://localhost:8080") await asyncio.Future() # Run forever if __name__ == '__main__': asyncio.run(main()) ``` **Key Considerations for MCP:** * **Specification Adherence:** Carefully review the MCP specification you're implementing. Pay close attention to the required data formats, error codes, and communication protocols. * **Error Handling:** Implement robust error handling to gracefully handle invalid requests, unexpected errors, and other issues. Return informative error messages to the client. * **Security:** Consider security implications, especially if your MCP server is exposed to the internet. Implement authentication, authorization, and input validation to protect against malicious attacks. * **Performance:** Optimize your code for performance, especially if you expect a high volume of requests. Use asynchronous programming effectively, and consider caching frequently accessed data. * **Scalability:** Design your MCP server to be scalable, so that it can handle increasing traffic. Consider using a load balancer and multiple instances of your server. * **Monitoring:** Implement monitoring to track the performance and health of your MCP server. Use metrics like request latency, error rates, and resource utilization to identify and resolve issues. This comprehensive list should give you a good starting point for building your MCP server on top of aiohttp. Remember to adapt the tools and techniques to the specific requirements of your MCP implementation.

ADB MCP Server

A Model Context Protocol server that provides Android Debug Bridge functionality for automating Android devices, enabling remote device management, screen operations, app management, file operations, and shell command execution.

Google Drive MCP Server

Enables read-only access to multiple Google Drive accounts simultaneously through secure service account authentication. Supports listing, searching, and extracting content from Google Workspace documents, spreadsheets, and text files.

2slides MCP Server

Enables users to generate presentation slides using 2slides.com's API through Claude Desktop. Supports searching for slide themes, generating slides from text input, and monitoring job status for slide creation.

MCP Weather Server

Enables users to get current weather information for any city worldwide using the Open-Meteo API. Provides temperature and wind speed data through natural language queries.

Discord MCP Server

A secure server that enables interaction with Discord channels through JWT-authenticated API calls, allowing users to send messages, fetch channel data, search content, and perform moderation actions.

NetBrain MCP

An open-source network operations integration platform that connects large language models with network devices through the Model Context Protocol, allowing AI assistants to perform network configuration, diagnostics, and management tasks.

defi-yields-mcp

An MCP server for AI agents to explore DeFi yield opportunities, powered by DefiLlama.

Webpage Design Analyzer

An MCP server that analyzes webpage design images using vision models and generates development documentation in Markdown format.

AI Research MCP Server

Enables real-time tracking of AI/LLM research progress by searching and aggregating content from arXiv, GitHub, Hugging Face, and Papers with Code. Supports intelligent search, automated daily/weekly research summaries, and covers 15+ AI research areas with smart caching.

Infrastructure Auto Provisioner

Infrastructure Auto Provisioner

Remote MCP Server (Authless)

Enables deployment of MCP servers without authentication on Cloudflare Workers. Provides a template for creating remote MCP servers that can be connected to clients like Claude Desktop or the Cloudflare AI Playground.

McFlow

Enables creation, management, and deployment of n8n workflows with enhanced context and automation capabilities. Features code extraction, automatic documentation, template generation, and intelligent project organization for streamlined workflow development.

OLEXI Australian Laws MCP Server

Enables AI agents to search AustLII (Australian Legal Information Institute) for real Australian legislation and case law. Provides citations with direct links for legal research and verification.

Aviation Model Context Protocol

Integration platform for aviation data sources including weather, NOTAMs, airport information, and flight planning APIs, enabling comprehensive pre-flight preparation and in-flight decision support.

YingDao RPA MCP Server

A Model Context Protocol server that enables AI platforms to invoke RPA (Robotic Process Automation) capabilities from YingDao, allowing automated execution of repetitive tasks through AI interactions.

supOS MCP Server

镜子 (jìng zi)

Weather MCP Server

Provides real-time weather information and forecasts, connecting AI assistants with live weather data for current conditions and multi-day forecasts for any location worldwide.

Hashkey MCP Server

A Model Context Protocol server that provides onchain tools for AI applications to interact with the Hashkey Network, enabling cryptocurrency transfers, smart contract deployment, and blockchain interactions.

Foundry MCP Server

一个 MCP 服务器，允许 AI 助手通过自然语言查询和命令与 Foundry 数据集、本体对象和函数进行交互。

MCP Chat Adapter

用于使用 OpenAI 兼容聊天端点的 MCP 服务器

Google Docs MCP Server

Connects Claude to Google Docs, allowing users to list, read, create, update, search, and delete documents in their Google Drive through natural language interactions.