发现优秀的 MCP 服务器

Reddit MCP Server

Enables interaction with Reddit through the Reddit API, allowing users to search posts, retrieve saved content, fetch comments, reply to comments, and access detailed post information with comment trees.

MCP Multi-Language Sandbox

Enables local, Docker-isolated code execution across six programming languages including Python, Rust, and TypeScript. It features pre-warmed container pooling, persistent sessions, and built-in support for machine learning libraries.

MCP Workflow Tracker

Provides observability for multi-agent workflows by tracking hierarchical task structure, architectural decisions, reasoning, encountered problems, code modifications with Git diffs, and temporal metrics.

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.

MCP_3

Provides paper search and management capabilities along with weather queries, demonstrating MCP integration for AI systems to interact with external resources and tools.

Webpage Design Analyzer

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

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.

Outlook OAuth MCP Server

Enables interaction with Microsoft Outlook for managing emails and calendars through OAuth2 delegated access. It provides a stateless, spec-compliant server that allows users to authenticate and perform mail and calendar operations with their own Microsoft accounts.

Gemini Thinking Server

Integrates Google's Gemini API to provide sequential analytical thinking and problem-solving capabilities with meta-commentary, confidence levels, branching thoughts, and session persistence for complex problems.

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.

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.

Minted MCP Server

Enables interaction with Minted.com to retrieve address book contacts, order history, and delivery information for recent card orders.

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.

MCP Stock Market

一个模型上下文协议工具，用于使用 Alpha Vantage API 获取任何股票代码的每日股市数据。

Gmail MCP Server

Enables AI assistants to read unread Gmail messages and compose draft replies while maintaining thread continuity. It includes optional Notion integration to fetch and apply specific style guides for consistent professional communication.

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.

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.

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.

MCPServer

一个简单的 MCP 服务器，用于启用代理工作流。 (Yī gè jiǎndān de MCP fúwùqì, yòng yú qǐyòng dàilǐ gōngzuò liú.) Alternatively, depending on the context, you might also use: 一个简单的 MCP 服务器，以实现基于代理的工作流程。(Yī gè jiǎndān de MCP fúwùqì, yǐ shíxiàn jīyú dàilǐ de gōngzuò liúchéng.) The first translation is more literal, while the second emphasizes the "agent-based" nature of the workflow. Choose the one that best fits the specific context.

Movie Search MCP Server

An MCP server that allows users to search for movies, get detailed information, receive genre-based recommendations, and discover popular/trending films using OMDb and TMDb APIs.

Containerized Strands Agents

Runs isolated AI agents in Docker containers with persistent workspaces and conversation history. Each agent has access to file operations, shell commands, GitHub integration, and can connect to external MCP servers for additional tools.

Whissle MCP Server

A Python-based server that provides access to Whissle API endpoints for speech-to-text, diarization, translation, and text summarization.

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.

Dev.to MCP Server

An MCP server implementation that allows AI assistants to access, search, and interact with Dev.to content, including fetching articles, retrieving user information, and publishing new content.

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.

MCP Memory System

Enables AI assistants to maintain persistent conversations and context between sessions through automated saving and global installation across projects. Provides zero-configuration memory persistence with automatic conversation history preservation.

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.

TypeScript MCP Server Boilerplate

A starter project designed to quickly build and deploy Model Context Protocol (MCP) servers using the TypeScript SDK and Zod for schema validation. It features example implementations for tools and resources, providing a solid foundation for custom MCP development and integration.

Infrastructure Auto Provisioner

Infrastructure Auto Provisioner

Date MCP Server

Provides AI assistants with accurate current date and day of week information through simple tools for retrieving ISO-formatted dates and day names.