发现优秀的 MCP 服务器

mcp-udacity-commit

An MCP server that validates and formats git commit messages according to the Udacity Git Commit Message Style Guide.

short-form-video-editor-mcp

Turns long-form videos into short-form clips (TikTok/Reels) by reasoning over word-timestamped transcripts, with silence-aware rendering, STT-based validation, and optional reframing/captions.

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.

mcpwatch

MCP server that audits other MCP servers. Run MCPWatch security scans from inside Claude Code or any MCP-compatible agent with 10 OWASP MCP Top 10 aligned checks and A-F letter grades.

Settled

Enables AI agents to query a team's decision ledger to check if past decisions are still binding before taking action, via the decisions:// MCP protocol.

Claude Bridge

Enables real-time cross-machine communication for Claude Code agents using a shared MCP relay server.

OpenFilings

Enables querying and retrieving normalized financial statements from 25 non-US markets via a local MCP server, with support for company search, filing listings, and structured balance sheet data.

nmlp-mcp

Hosted MCP server for antiquarian first-edition identification and New Mexico book-donation logistics. 12 tools over a CC-BY, DOI-cited dataset of 6,700+ titles and 870 publisher conventions.

flyu-design

Enables AI assistants to directly view, understand, and modify local Sketch/Figma design files, including writing code, applying semantic edits, and exporting assets, all through a local MCP server without uploading data.

Exa Documentation MCP Server

Provides instant access to Exa's neural search API documentation, code examples, and integration guides through natural language queries, enabling faster development of search, news monitoring, and RAG applications.

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.

mcp-server-anki

Enables AI tools to manage Anki flashcards, including deck management, card search, creation, editing, deletion, and statistics.

wardcat-mcp

MCP server providing on-prem PII detection and anonymization tools (scan and is_sensitive) for AI agents, ensuring data stays local.

dutch-property-context

Enables querying Dutch property context for an address, returning building, energy, neighborhood, environment, heritage, and school data from public registers, with explicit match verification and signals.

design-doc-mcp

Turns real technical work from a Claude Code session into a saved, professional design doc with one slash command, no copy-pasting or signup required.

Foundry Agents MCP Server

Exposes Azure AI Foundry agents, workflows, and AI Search vector-database capabilities as MCP tools, enabling natural language interaction with agents, semantic search, and index management.

blender-mcp-ultra

MCP server providing 138+ tools to control Blender 4.2 LTS, enabling modeling, coloring, rigging, animation, geometry nodes, UV, printing, batch operations, and IO through natural language from any AI assistant.

GenRocket MCP

GenRocket MCP server that exposes GenRocket as tools for AI chat (GitHub Copilot), enabling natural-language interaction to test connections, list projects/scenarios/chains/domains/generators, download .grs scenarios, and run the GenRocket Runtime to generate data.

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.

Maito MCP Server

Enables AI assistants to interact with Maito workspaces for task management, note-taking, and planning.

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.

Minted MCP Server

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

Karya

An MCP server that gives AI agents hands to make phone calls, send messages, and handle CRM/task workflows. It runs with a simulated provider for zero-cost testing and can switch to real providers like Twilio and ElevenLabs.

MCP RepoBridge

Enables safe repository inspection and Docker-sandboxed command execution via MCP, with optional Codex handoff for implementation tasks.

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.

armavita-quo-mcp

Local-first MCP server for Quo/OpenPhone automation across messaging, calls, contacts, users, phone numbers, and webhooks via a clean stdio tool surface.

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.

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.

Fix Memory MCP

Local-first error memory for AI coding agents, enabling them to search past fixes before attempting new repairs and save verified cases as Markdown.

Godot MCP

Model Context Protocol server for Godot Engine providing 279 tools across 26 categories for AI assistants to read, inspect, and modify Godot projects via stdio transport.