发现优秀的 MCP 服务器

zora-mcp-server

An MCP server that enables AI-driven on-chain interactions with the Zora Protocol on Base, supporting token queries, swaps, and transfers via natural language.

munich-mensa-mcp

Remote MCP Server for listing and getting the menus of the official mensas in munich

Apache AGE MCP Server

Enables AI agents to manage and interact with Apache AGE graph databases through natural language. Supports creating, updating, querying, and visualizing multiple graphs with vertices and edges.

image_studio_mcp

An MCP server that lets any MCP client generate and edit images using Image Studio API, returning results inline and saving PNGs to disk.

crawl4ai-mcp

Okay, here's a Python outline and explanation of how you might structure an MCP (Model Context Protocol) server that wraps the Crawl4AI library. This is a conceptual framework; you'll need to fill in the details based on your specific requirements and the Crawl4AI library's API. **Conceptual Overview** 1. **MCP Server:** This will be the main entry point. It listens for requests conforming to the MCP standard. These requests will specify which Crawl4AI function to execute and provide the necessary parameters. 2. **Crawl4AI Wrapper:** This layer translates MCP requests into calls to the Crawl4AI library. It handles parameter conversion, error handling, and result formatting. 3. **Crawl4AI Library:** This is the core library that performs the actual crawling and AI-related tasks. **Python Code Outline** ```python # Import necessary libraries import json from http.server import BaseHTTPRequestHandler, HTTPServer # For a simple HTTP server # Or use a more robust framework like Flask or FastAPI # from flask import Flask, request, jsonify # Example with Flask # Assuming Crawl4AI is installed and importable import crawl4ai # Replace with the actual import statement # --- Crawl4AI Wrapper --- class Crawl4AIWrapper: def __init__(self): # Initialize any necessary Crawl4AI resources here pass def crawl_website(self, url, max_depth=1): """ Wraps the Crawl4AI website crawling function. Args: url (str): The URL to start crawling from. max_depth (int): The maximum depth to crawl. Returns: dict: A dictionary containing the crawling results. Format this according to your MCP requirements. Could include: - `status`: "success" or "error" - `data`: The crawled data (e.g., list of URLs, extracted text) - `error_message`: If an error occurred. """ try: # Call the Crawl4AI function results = crawl4ai.crawl_website(url, max_depth=max_depth) # Replace with actual Crawl4AI call # Format the results into an MCP-compatible dictionary response = { "status": "success", "data": results # Adapt this to the MCP format } return response except Exception as e: # Handle errors gracefully response = { "status": "error", "error_message": str(e) } return response def analyze_text(self, text): """ Wraps the Crawl4AI text analysis function. Args: text (str): The text to analyze. Returns: dict: A dictionary containing the analysis results. """ try: analysis_results = crawl4ai.analyze_text(text) # Replace with actual Crawl4AI call response = { "status": "success", "data": analysis_results } return response except Exception as e: response = { "status": "error", "error_message": str(e) } return response # Add more wrapper functions for other Crawl4AI functionalities # --- MCP Server (Simple HTTP Server Example) --- class MCPRequestHandler(BaseHTTPRequestHandler): def do_POST(self): """Handles POST requests (MCP requests).""" content_length = int(self.headers['Content-Length']) post_data = self.rfile.read(content_length) try: request_data = json.loads(post_data.decode('utf-8')) # Process the request response = self.process_request(request_data) # Send the response self.send_response(200) # OK self.send_header('Content-type', 'application/json') self.end_headers() self.wfile.write(json.dumps(response).encode('utf-8')) except json.JSONDecodeError: self.send_response(400) # Bad Request self.send_header('Content-type', 'application/json') self.end_headers() self.wfile.write(json.dumps({"status": "error", "error_message": "Invalid JSON"}).encode('utf-8')) except Exception as e: self.send_response(500) # Internal Server Error self.send_header('Content-type', 'application/json') self.end_headers() self.wfile.write(json.dumps({"status": "error", "error_message": str(e)}).encode('utf-8')) def process_request(self, request_data): """ Processes the MCP request and calls the appropriate Crawl4AI function. Args: request_data (dict): The JSON-decoded MCP request. This should contain information like the function name and parameters. Returns: dict: The response from the Crawl4AI wrapper. """ global crawl4ai_wrapper # Access the global instance try: function_name = request_data.get("function") parameters = request_data.get("parameters", {}) # Default to empty dict if function_name == "crawl_website": url = parameters.get("url") max_depth = parameters.get("max_depth", 1) # Default max_depth if not url: return {"status": "error", "error_message": "Missing 'url' parameter"} return crawl4ai_wrapper.crawl_website(url, max_depth) elif function_name == "analyze_text": text = parameters.get("text") if not text: return {"status": "error", "error_message": "Missing 'text' parameter"} return crawl4ai_wrapper.analyze_text(text) else: return {"status": "error", "error_message": "Invalid function name"} except Exception as e: return {"status": "error", "error_message": str(e)} def run_server(server_class=HTTPServer, handler_class=MCPRequestHandler, port=8000): """Starts the MCP server.""" server_address = ('', port) httpd = server_class(server_address, handler_class) print(f"Starting MCP server on port {port}") httpd.serve_forever() # --- Main --- if __name__ == "__main__": # Initialize the Crawl4AI wrapper crawl4ai_wrapper = Crawl4AIWrapper() # Create a global instance # Start the server run_server() # --- Example MCP Request (JSON) --- # { # "function": "crawl_website", # "parameters": { # "url": "https://www.example.com", # "max_depth": 2 # } # } # --- Example MCP Request (JSON) --- # { # "function": "analyze_text", # "parameters": { # "text": "This is some text to analyze." # } # } ``` **Key Improvements and Explanations** * **Error Handling:** Includes `try...except` blocks to catch potential errors during Crawl4AI calls and JSON processing. Returns error messages in the MCP response. * **Parameter Handling:** The `process_request` function extracts parameters from the JSON request and passes them to the Crawl4AI wrapper functions. It also includes default values for optional parameters. It checks for missing required parameters. * **MCP-Compliant Responses:** The responses are formatted as JSON dictionaries with a `status` field ("success" or "error") and either a `data` field (for successful results) or an `error_message` field. Adapt the `data` format to your specific MCP requirements. * **Function Dispatch:** The `process_request` function uses `if/elif/else` to dispatch the request to the correct Crawl4AI wrapper function based on the `function` field in the MCP request. * **Crawl4AI Wrapper Class:** Encapsulates the Crawl4AI library calls within a class. This allows you to initialize resources (e.g., API keys, models) in the `__init__` method and reuse them across multiple requests. * **Global Crawl4AI Wrapper Instance:** A global instance `crawl4ai_wrapper` is created to avoid re-initializing the wrapper for each request. This can improve performance if the wrapper initialization is expensive. * **Example MCP Requests:** Includes example JSON requests that you can use to test the server. * **Clearer Structure:** Separates the Crawl4AI wrapper logic from the MCP server logic for better organization. * **Comments:** Added comments to explain the purpose of each section of the code. * **Uses `json` library:** Uses the standard `json` library for encoding and decoding JSON data. * **HTTP Status Codes:** Returns appropriate HTTP status codes (200, 400, 500) to indicate the success or failure of the request. * **Flexibility:** The code is designed to be easily extended to support more Crawl4AI functions. Just add more wrapper functions to the `Crawl4AIWrapper` class and update the `process_request` function to handle the new function names. **How to Use** 1. **Install Crawl4AI:** Make sure you have the Crawl4AI library installed (`pip install crawl4ai` or however it's installed). *Replace `crawl4ai` with the actual package name if it's different.* 2. **Replace Placeholders:** Replace the placeholder `crawl4ai.crawl_website()` and `crawl4ai.analyze_text()` calls with the actual calls to the Crawl4AI library. Adapt the parameter passing and result formatting to match the Crawl4AI API. 3. **Define MCP Format:** Clearly define the format of your MCP requests and responses. The code assumes a JSON-based format with a `function` field and a `parameters` field. 4. **Run the Server:** Run the Python script. It will start an HTTP server on port 8000 (by default). 5. **Send MCP Requests:** Send HTTP POST requests to the server with the MCP requests in the body. Use a tool like `curl`, `Postman`, or a Python `requests` library. **Example using `curl`:** ```bash curl -X POST -H "Content-Type: application/json" -d '{ "function": "crawl_website", "parameters": { "url": "https://www.example.com", "max_depth": 1 } }' http://localhost:8000 ``` **Important Considerations** * **Security:** This is a *very basic* HTTP server. For production environments, use a more robust framework like Flask or FastAPI, and implement proper security measures (authentication, authorization, input validation, etc.). * **Asynchronous Operations:** If Crawl4AI operations are long-running, consider using asynchronous programming (e.g., `asyncio` with FastAPI) to avoid blocking the server. * **Scalability:** For high-volume traffic, you'll need to consider scalability. This might involve using a load balancer, multiple server instances, and a message queue for handling requests. * **Error Logging:** Implement proper error logging to help you debug and monitor the server. * **MCP Standard:** Ensure that your implementation fully conforms to the MCP standard. This includes the request and response formats, error codes, and any other requirements. * **Crawl4AI API:** Thoroughly understand the Crawl4AI library's API and how to use its functions effectively. * **Rate Limiting:** Implement rate limiting to prevent abuse of your server and to comply with the terms of service of the websites you are crawling. * **User Agent:** Set a proper user agent string when crawling websites to identify your crawler and avoid being blocked. * **Robots.txt:** Respect the `robots.txt` file of the websites you are crawling. **Chinese Translation of Key Terms** * **MCP (Model Context Protocol):** 模型上下文协议 (Móxíng Shàngxiàwén Xiéyì) * **Crawl4AI:** 网络爬虫AI库 (Wǎngluò Páchóng AI Kù) or AI爬虫库 (AI Páchóng Kù) * **Server:** 服务器 (Fúwùqì) * **Wrapper:** 封装器 (Fēngzhuāngqì) or 包装器 (Bāozhuāngqì) * **Function:** 函数 (Hánshù) * **Parameter:** 参数 (Cānshù) * **Request:** 请求 (Qǐngqiú) * **Response:** 响应 (Xiǎngyìng) * **Error:** 错误 (Cuòwù) * **Status:** 状态 (Zhuàngtài) * **Data:** 数据 (Shùjù) * **URL:** 网址 (Wǎngzhǐ) * **Text:** 文本 (Wénběn) * **Analysis:** 分析 (Fēnxī) * **JSON:** JSON (JSON) (commonly used without translation) * **HTTP:** HTTP (HTTP) (commonly used without translation) This comprehensive outline should give you a solid foundation for building your MCP server with Crawl4AI. Remember to adapt the code to your specific needs and the Crawl4AI library's API. Good luck!

Levels of Self

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Marvel MCP Server using Azure Functions

一个基于 Azure Functions 的 MCP 服务器，它允许通过官方 Marvel Developer API 与漫威角色和漫画数据进行交互。

PrivateGPT MCP Server

镜子 (jìng zi)

aigently

Securely feeds summarized expert security rules into your coding assistance Claude Code, Cursor, etc — zero config, no API key.

serial-bridge-mcp

Enables sharing two serial consoles between a local operator and MCP agents, allowing command execution and status monitoring on connected devices.

mcpCut

A real video editor for AI agents, served over MCP, enabling journaled timeline editing, rendering via FFmpeg/MLT, and deterministic CLI operation.

DeskMail AI

Connects Claude Desktop to a local email client, enabling AI-powered email reading, searching, drafting, and organization while preventing sending or permanent deletion.

@1claw/mcp

MCP server for secure, just-in-time secret retrieval from 1claw vault and malicious content inspection, enabling AI agents to access secrets and security analysis.

SSH MCP Server

An MCP server that lets an AI assistant run commands and transfer files over SSH without exposing connection details.

Mnemo

MCP server providing a persistent, temporal memory brain for AI assistants, enabling cross-session recall of facts with relationships and temporal context.

athenahealth MCP Server

Enables AI-powered clinical decision support by integrating with athenahealth's API to access patient data, manage prescriptions, check drug interactions, and generate clinical assessments. Provides HIPAA-compliant healthcare workflows with comprehensive audit logging and data sanitization.

mcp-memory

Provides persistent memory with semantic search for MCP-based AI agents, enabling them to store and recall information across sessions using vector embeddings.

MCP Commute Assistant

A smart commute assistant that monitors travel routes via the Amap API and sends automated notifications to DingTalk. It enables users to schedule daily route checks and receive real-time traffic updates using a modular MCP-based architecture.

URL Agent

MCP server for intelligent web crawling using a ReAct agent. Adaptively explores websites and returns structured analysis.

legal-kb-builder

A local legal knowledge base construction factory that provides a consultation agent with multi-source retrieval, supporting MCP server for external services.

JobScout MCP

A privacy-first MCP server that searches, normalizes, and deduplicates job listings across multiple providers, enabling trustworthy discovery without scoring or applying.

hypernmnesia-mcp-viz

A read-only, cross-platform visualization layer for Cortex that renders memory, sessions, and code into six live reading angles (graph galaxy, 3D anatomical brain, execution trace, consolidation board, knowledge browser, and wiki) without ever writing a memory.

Figma MCP Go

Figma MCP server with full read/write access via plugin bridge — no API token, no rate limits. 83 tools for design automation: styles, variables, components, prototypes, and content.

SentinelMCP

Automated red-teaming and reliability-auditing for AI agents, exposed as an MCP server. It attacks and scores agents for prompt injection, tool misuse, exfiltration, and unreliable behavior.

perplexity-server

A TypeScript-based MCP server that implements a simple notes system with resources, tools for creating notes, and prompts for summarization.

memex

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Lusha MCP Plugin

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Paperless MCP

Enables searching, tagging, uploading, and reading documents in Paperless-NGX, with management of tags, correspondents, document types, and custom fields via MCP tools and resources.