发现优秀的 MCP 服务器

Scientific Search MCP for Cursor

用于搜索科学资源的 MCP 服务器。由 AI 为 AI 打造的 Vibe。

🤖 MCP Server Terminator Test Facility

MCP服务器终结者的试验场。后会有期，漏洞们！

Mcp Server Trfrmarket

一个基于流行的 R 语言包和一些其他装饰的 MCP 服务器，用于转会市场。

MCP Document Server

一个简单的服务器，实现了用于文档搜索的模型上下文协议。

task

一个 MCP 服务器，用于暴露一种数据格式，以便将翻译任务转化为环境行动。 Or, a slightly more formal and technical translation: 一个 MCP 服务器，用于暴露一种数据格式，以将翻译任务转化为环境行为。

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!

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.)

EVM MCP Server

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Ethereum RPC MPC Server

一个 TypeScript MCP 服务器，利用 MCP SDK 来支持所有以太坊 JSON-RPC 调用，从而使 AI 模型能够与区块链数据进行交互。

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。

MCP Servers Schemas

本文档提供了一系列不同的 MCP 服务器列表。对于每个服务器，我们都提供了一个模式定义，其中包括最新的基本信息和工具定义。

OmniLLM: Universal LLM Bridge for Claude

OmniLLM：一个模型上下文协议（MCP）服务器，使 Claude 能够访问和整合来自多个大型语言模型（LLM）的响应，包括 ChatGPT、Azure OpenAI 和 Google Gemini，从而创建一个统一的 AI 知识中心。

Weather Mcp

天气预报和警报的 MCP 服务器 (Tiānqì yùbào hé jǐngbào de MCP fúwùqì) Alternatively, depending on the context, you could also say: 用于天气预报和警报的 MCP 服务器 (Yòng yú tiānqì yùbào hé jǐngbào de MCP fúwùqì) - This emphasizes the *purpose* of the server. Which one is more appropriate depends on the specific situation. The first one is a more direct translation.

Perplexity AI MCP Server

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MCPilled

MCPilled.com 是一个精选的关于 MCP 服务器、客户端、协议更新以及所有其他 MCP 相关内容的新闻集合。

MCP Gnews

MCP服务器为客户端提供在互联网上搜索相关新闻的能力。

Skynet-MCP (THIS PROJECT IS A WORK IN PROGRESS)

一个充当代理的 MCP 服务器，并且可以通过使用 MCP 来生成更多的代理……MCP 嵌套！

tagesschau-mcp-server

这是一个用于 tagesschau.de 的 MCP 服务器。

RAG-MCP Pipeline Research

一个学习仓库，探索使用免费和开源模型实现的检索增强生成 (RAG) 和多云处理 (MCP) 服务器集成。

DataGovMy MCP Server

一个 NodeJS TypeScript MCP 服务器，旨在从 datagov.my 获取外汇汇率数据，并执行外币与 MYR 之间的双向货币转换。

Earn With AI

以下是一些你可以用来轻松创造收入的开源人工智能项目列表： **内容创作类 (内容创作和自动化):** * **GPT-2 / GPT-3 (通过 API 访问) / Llama 2 (本地部署):** 虽然 GPT-3 需要通过 OpenAI 的 API 访问，但 GPT-2 和 Llama 2 可以本地部署。你可以使用这些模型来： * **撰写文章、博客文章、社交媒体帖子:** 提供内容创作服务，帮助企业或个人生成高质量的内容。 * **生成产品描述:** 为电商网站或在线商店创建引人注目的产品描述。 * **编写代码注释或文档:** 帮助开发者提高代码的可读性和可维护性。 * **创建故事、诗歌或剧本:** 为娱乐行业或个人提供创意写作服务。 * **构建聊天机器人:** 创建客户服务聊天机器人或虚拟助手。 * **收费 API 访问:** 如果你有足够的计算资源，可以搭建自己的 API 服务，并向其他开发者收费。 * **Stable Diffusion / DALL-E 2 (通过 API 访问) / Midjourney (通过 API 访问):** 这些是强大的图像生成模型。你可以使用它们来： * **创建艺术作品:** 生成独特的艺术作品，并在在线市场或画廊中出售。 * **设计营销材料:** 为企业或个人创建引人注目的广告、海报和社交媒体图像。 * **生成游戏资产:** 为游戏开发者创建角色、场景和道具。 * **提供图像编辑服务:** 使用 AI 技术来增强、修复或修改图像。 * **创建定制头像:** 为用户生成个性化的头像。 * **Whisper (语音转文本):** OpenAI 的 Whisper 模型可以将语音转换为文本。你可以使用它来： * **提供转录服务:** 将音频或视频文件转录为文本。 * **创建字幕:** 为视频添加字幕。 * **构建语音助手:** 开发语音控制的应用程序或设备。 * **提供语音分析服务:** 分析语音数据，例如识别情绪或关键词。 **自动化和效率提升类:** * **TensorFlow / PyTorch (机器学习框架):** 这些是流行的机器学习框架，你可以使用它们来： * **构建预测模型:** 预测销售额、客户流失率或其他关键指标。 * **开发自动化系统:** 自动化重复性任务，例如数据输入或报告生成。 * **提供数据分析服务:** 分析数据并提供有价值的见解。 * **构建定制 AI 解决方案:** 为特定行业或企业开发定制的 AI 解决方案。 * **Scikit-learn (机器学习库):** 一个易于使用的机器学习库，可以用于： * **构建分类器:** 将数据分类到不同的类别中。 * **构建回归模型:** 预测连续值。 * **进行聚类分析:** 将数据分组到不同的集群中。 **重要提示:** * **法律和伦理问题:** 在使用 AI 模型时，请务必考虑法律和伦理问题，例如版权、隐私和偏见。 * **计算资源:** 某些 AI 模型需要大量的计算资源才能运行。你需要考虑你的硬件和软件需求。 * **技能要求:** 你需要具备一定的编程和机器学习技能才能有效地使用这些 AI 项目。 * **市场调研:** 在开始任何项目之前，请进行市场调研，以确定是否有需求。 * **持续学习:** AI 领域发展迅速，你需要不断学习新的技术和工具。 **如何开始:** 1. **选择一个你感兴趣的项目。** 2. **学习相关的技术和工具。** 3. **构建一个原型或 MVP (最小可行产品)。** 4. **测试你的产品并收集反馈。** 5. **改进你的产品并将其推向市场。** 祝你成功！

StableMCP

一个简单的 MCP 服务器，用于使用 Stable Diffusion 生成图像。 (Yī gè jiǎndān de MCP fúwùqì, yòng yú shǐyòng Stable Diffusion shēngchéng túxiàng.)

HotNews MCP Server

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MCP Partner Hub

一个用于发现和比较来自独立软件供应商 (ISV) 合作伙伴的 Model Context Protocol (MCP) 服务器的集中式存储库。

testmcpgithubdemobypurnatwo

从 MCP 服务器演示创建。

MCP Telemetry

可观测性有所帮助。此 MCP 服务器为你在 Claude（或合适的 MCP 客户端）上的所有对话添加追踪功能，以便你可以追踪、理解、调试和报告你的所有互动。

Serveur MCP Simple pour Service d'Heure

一个简单的MCP服务器，提供当前时间服务。