发现优秀的 MCP 服务器

MCP Argentina Datos

A Model Context Protocol server that provides tools to access information about Argentina through the Argentina Datos API, including holidays, presidential events, dollar exchange rates, and legislative data.

Figma MCP PRO

Professional Model Context Protocol server that enables AI-optimized Figma design analysis and comprehensive design-to-code conversion through a structured 5-step workflow.

Florence-2 MCP Server

一个使用 Florence-2 处理图像的 MCP 服务器。

Elysia MCP Starter

A template for building Model Context Protocol servers using Elysia and Bun runtime, enabling LLM clients like Claude Desktop and Cody to access custom tools, prompts, and data resources.

lol-client-mcp Public

一个用于访问《英雄联盟》客户端数据的 MCP (模型-控制器-处理器) 服务器。该服务器提供了一系列工具，这些工具与《英雄联盟》实时客户端数据 API 通信，以检索游戏内数据。

Soccer MCP Server

通过 API-Football 提供对全面足球统计数据和实时比赛数据的程序化访问，使应用程序能够检索联赛排名、球队赛程、球员统计数据和实时比赛事件。

DNDzgz MCP Server

An MCP server that provides real-time information about the Zaragoza tram system, including arrival estimations and station details through the DNDzgz API.

Tavily Web Search MCP Server

Enables web search capabilities through the Tavily API via the Model Context Protocol. Allows users to perform web searches and retrieve information from the internet through natural language queries.

WSB Analyst MCP Server

A Model Context Protocol server that enables LLM clients to fetch, analyze, and extract insights from real-time WallStreetBets posts, comments, and shared links for market analysis.

MSPaint MCP Server with AI-based Planning Algorithms

使用高级 AI 提示来增强 LLM 规划，以解决复杂的数学问题并在画图画布上绘制答案。 (Shǐyòng gāojí AI tíshì lái zēngqiáng LLM guīhuà, yǐ jiějué fùzá de shùxué wèntí bìng zài huàtú huàbù shàng huìzhì dá'àn.)

Klarna Payments MCP Server

An MCP server that enables interaction with Klarna's Payments API, allowing applications to implement Klarna payment options through natural language commands.

filesystem-mcp

一个基于 TypeScript 的 MCP 服务器，实现了一个简单的笔记系统，允许用户通过 URI 和工具创建、访问和生成文本笔记的摘要。

MCP

Okay, here's a breakdown of how you might configure an MCP (presumably a "Management Console Platform" or similar) server to view company information and stock prices using Claude (likely referring to Anthropic's Claude AI model). This is a conceptual outline, as the specific steps will depend heavily on the MCP software you're using. **I. Understanding the Components** * **MCP Server:** This is the central system that manages and displays information. It likely has a database, a user interface, and some form of scripting or configuration capabilities. * **Claude AI:** This is the AI model that will provide the company information and stock price data. You'll need to interact with it through its API. * **Data Sources:** Claude will need access to reliable data sources for company information and stock prices. This might include: * **Financial APIs:** APIs like Alpha Vantage, IEX Cloud, or Finnhub provide real-time and historical stock data. * **Company Information Databases:** APIs or databases like Crunchbase, Clearbit, or even Wikipedia can provide company descriptions, industry information, and key personnel. **II. High-Level Steps** 1. **Choose and Set Up Data Sources:** * **Select APIs:** Research and choose the financial and company information APIs that best suit your needs (consider cost, data coverage, and ease of use). * **API Keys:** Obtain API keys from the chosen providers. Store these securely (e.g., using environment variables or a secrets management system). 2. **Develop an API Integration Layer (Middleware):** * **Purpose:** This layer acts as an intermediary between your MCP server and the Claude API and data sources. It handles: * **API Calls:** Making requests to the financial and company information APIs. * **Data Formatting:** Transforming the data from the APIs into a format that Claude can understand. * **Error Handling:** Managing API errors and retries. * **Rate Limiting:** Respecting the API rate limits to avoid being blocked. * **Technology:** You can build this layer using languages like Python, Node.js, or Java. Python is often a good choice due to its rich ecosystem of libraries for data science and API interaction. 3. **Integrate with Claude AI:** * **Claude API Access:** Obtain access to the Claude API (you'll likely need to sign up for an account with Anthropic). * **Prompt Engineering:** Craft effective prompts for Claude to extract the desired information. For example: * "Summarize the key financial information for [Company Name] based on the following data: [Financial Data]." * "Provide a brief overview of [Company Name], including its industry, key products, and recent news." * "What is the current stock price of [Stock Ticker]?" * **API Calls to Claude:** Send the formatted data and prompts to the Claude API. * **Response Handling:** Parse the response from Claude and extract the relevant information. 4. **Configure the MCP Server:** * **Data Display:** Design the user interface within your MCP to display the company information and stock prices. * **Data Retrieval:** Configure the MCP to call your API integration layer to retrieve the data. This might involve: * **Scripting:** Using the MCP's scripting language (if it has one) to make HTTP requests to your API. * **Plugins/Extensions:** Developing a plugin or extension for the MCP that handles the data retrieval and display. * **User Interface:** Create a user-friendly interface where users can search for companies and view their information. 5. **Testing and Refinement:** * **Thorough Testing:** Test the entire system with a variety of companies and stock tickers to ensure accuracy and reliability. * **Prompt Optimization:** Refine your prompts to Claude to improve the quality of the responses. * **Error Handling:** Implement robust error handling to gracefully handle API failures and other issues. * **Performance Tuning:** Optimize the performance of the system to ensure that data is retrieved and displayed quickly. **III. Example Implementation (Conceptual - Python with Flask)** This is a simplified example to illustrate the concepts. You'll need to adapt it to your specific MCP and data sources. ```python # Python (Flask) API Integration Layer from flask import Flask, request, jsonify import requests import os app = Flask(__name__) # API Keys (replace with your actual keys) ALPHAVANTAGE_API_KEY = os.environ.get("ALPHAVANTAGE_API_KEY") CLAUDE_API_KEY = os.environ.get("CLAUDE_API_KEY") # Function to get stock price from Alpha Vantage def get_stock_price(ticker): url = f"https://www.alphavantage.co/query?function=GLOBAL_QUOTE&symbol={ticker}&apikey={ALPHAVANTAGE_API_KEY}" try: response = requests.get(url) response.raise_for_status() # Raise HTTPError for bad responses (4xx or 5xx) data = response.json() price = data['Global Quote']['05. price'] return price except requests.exceptions.RequestException as e: print(f"Error fetching stock price: {e}") return None except KeyError: print("Error: Could not parse stock price data.") return None # Function to get company information from Claude (using a placeholder) def get_company_info(company_name): # In a real implementation, you'd fetch data from a company information API # and then use Claude to summarize it. This is a simplified example. prompt = f"Provide a brief overview of {company_name}." # Replace with actual Claude API call # response = requests.post("CLAUDE_API_ENDPOINT", headers={"Authorization": f"Bearer {CLAUDE_API_KEY}"}, json={"prompt": prompt}) # company_info = response.json()["text"] company_info = f"This is placeholder information for {company_name}." return company_info @app.route("/company_data") def get_company_data(): company_name = request.args.get("company") ticker = request.args.get("ticker") if not company_name or not ticker: return jsonify({"error": "Company name and ticker are required."}), 400 stock_price = get_stock_price(ticker) company_info = get_company_info(company_name) if stock_price is None: return jsonify({"error": "Could not retrieve stock price."}), 500 data = { "company_name": company_name, "ticker": ticker, "stock_price": stock_price, "company_info": company_info, } return jsonify(data) if __name__ == "__main__": app.run(debug=True) ``` **Explanation of the Python Example:** * **Flask:** A lightweight web framework for creating the API. * **API Keys:** The code retrieves API keys from environment variables (a secure way to store them). **Important:** Never hardcode API keys directly into your code. * **`get_stock_price()`:** Fetches the stock price from the Alpha Vantage API. It includes error handling for API requests and data parsing. * **`get_company_info()`:** This is a placeholder. In a real implementation, you would: 1. Fetch company data from a company information API (e.g., Crunchbase). 2. Construct a prompt for Claude that includes the company data. 3. Send the prompt to the Claude API. 4. Parse the response from Claude to extract the company overview. * **`/company_data` endpoint:** This endpoint takes the company name and ticker as query parameters. It calls the `get_stock_price()` and `get_company_info()` functions and returns the data as a JSON response. * **Error Handling:** The code includes basic error handling to catch API errors and missing data. **How to Use the Example with Your MCP:** 1. **Deploy the API:** Deploy the Python Flask API to a server (e.g., using Heroku, AWS, or Google Cloud). 2. **Configure Your MCP:** In your MCP, you would need to: * Create a user interface element (e.g., a search box) where users can enter the company name and ticker. * Use the MCP's scripting language or plugin system to make an HTTP request to your API endpoint (e.g., `http://your-api-server/company_data?company=Apple&ticker=AAPL`). * Parse the JSON response from the API and display the data in the MCP's user interface. **IV. Important Considerations** * **Security:** Protect your API keys and ensure that your API is secure. Consider using authentication and authorization to control access to the API. * **Scalability:** If you expect a large number of users, you'll need to design your API to be scalable. Consider using a load balancer and caching to improve performance. * **Cost:** Be aware of the costs associated with using the Claude API and the financial and company information APIs. Monitor your usage and set limits to avoid unexpected charges. * **Data Accuracy:** The accuracy of the data depends on the quality of the data sources and the effectiveness of your prompts to Claude. Verify the data and consider using multiple data sources to improve accuracy. * **Claude API Limitations:** Be aware of Claude's context window limits and other API limitations. You may need to break down large requests into smaller chunks. * **Prompt Engineering:** Experiment with different prompts to Claude to get the best results. Consider using techniques like few-shot learning to improve the accuracy and relevance of the responses. * **Rate Limiting:** All APIs have rate limits. Implement proper rate limiting in your API integration layer to avoid being blocked. **In Summary** Integrating Claude with your MCP to view company information and stock prices involves: 1. Setting up data sources (financial and company information APIs). 2. Creating an API integration layer to handle API calls, data formatting, and error handling. 3. Integrating with the Claude API to generate summaries and insights. 4. Configuring your MCP to retrieve and display the data. 5. Thoroughly testing and refining the system. Remember to adapt this outline to the specific capabilities of your MCP server and the requirements of your application. Good luck!

RTC MCP Server

用于管理阿里云实时计算 Flink 资源的模型上下文协议 (MCP) 服务器实现

Lilith Shell

一个增强型的 MCP 服务器，赋予 AI 助手在用户系统上执行终端命令的能力，同时具有改进的安全控制，专为受控环境设计。

MCPolice

Enables AI tools to report violations of international laws to monitoring agencies like Interpol, UN, and IAEA. Provides tools for reporting violations, listing legal statutes, and retrieving violation statistics through the MCP protocol.

MCP Gateway

聚合和提供多个 MCP 服务器的 MCP 网关

SAP Documentation MCP Server

Provides offline access to SAP documentation and real-time SAP Community content, integrating official documentation with community-driven solutions for comprehensive developer support.

Multi Database MCP Server

多数据库 MCP 服务器是数据库模型上下文协议的高性能实现，旨在彻底改变 AI 代理与数据库交互的方式。目前支持 MySQL 和 PostgreSQL 数据库。

mcp-test-server

A lightweight MCP test server for verifying client connectivity, providing tools, resources, and prompts for integration.

Linkedin-Profile-Analyzer

一个强大的 LinkedIn 个人资料分析器，与 Claude AI 无缝集成，可以获取和分析公开的 LinkedIn 个人资料，使用户能够通过 RapidAPI 的 LinkedIn 数据 API 提取、搜索和分析帖子数据。

Mcp Pallete

一个简单的玩具版 MCP 服务器，可以获取图像颜色并从中生成 PNG 调色板。

ProjectDocHelper

ProjectDocHelper 是一个 MCP (模型上下文协议) 服务器，旨在自动生成项目文档，并通过 MCP 使 AI 开发工具（如 Cursor）可以访问这些文档，从而提高 AI 响应的准确性和相关性。

Elasticsearch MCP Server

通过 Model Context Protocol 服务器和自然语言命令，方便用户与 Elasticsearch 集群进行交互，执行索引操作、文档搜索和集群管理。

EVM MCP Server

镜子 (jìng zi)

MCP JSON Server

Enables comprehensive JSON file operations including reading, writing, transforming, and analyzing JSON data from local files and URLs. Supports JSON pointer access, schema inference, data merging, and specialized market data optimization tools.

MySQL MCP Server

A Message Control Protocol server that provides an interface for managing and querying departmental budget information through a MySQL database.