Discover Awesome MCP Servers

PostgreSQL MCP Server

Enables secure querying of PostgreSQL databases through MCP-compatible clients. Supports read-only SQL execution, table exploration, and connection management with built-in security validation.

PentestThinkingMCP

An AI-powered penetration testing reasoning engine that provides automated attack path planning, step-by-step guidance for CTFs/HTB challenges, and tool recommendations using Beam Search and MCTS algorithms.

Reddit MCP Server

Enables AI agents to search, read, and analyze Reddit content, including posts, comments, subreddits, and user profiles using natural language commands. It provides atomic tools for interacting with the Reddit API to retrieve trending topics and community metadata.

Bitwig MCP Server

Bitwig Studio 的 MCP 服务器

Directmedia MCP

Provides programmatic access to the Directmedia Publishing 'Digitale Bibliothek' collection, a 1990s German electronic book library containing 101 volumes of classic literature and philosophy with text extraction, search, and navigation capabilities.

mcp-skill-server

Build agent skills where you work — write a script, add a SKILL.md, and use it in Claude Code/codex/Cursor immediately. The same fixed entry point that runs locally deploys to production without a rewrite.

Sample Model Context Protocol Demos

好的，这是关于如何将模型上下文协议与 AWS 结合使用的一些示例： **总览** 模型上下文协议 (Model Context Protocol, MCP) 是一种标准化方法，用于将上下文信息传递给机器学习模型。这使得模型能够根据手头的任务更好地理解和响应。在 AWS 环境中，MCP 可以与各种服务集成，以增强模型的性能和可解释性。 **使用场景和示例** 以下是一些使用 MCP 与 AWS 结合的常见场景和示例： * **个性化推荐:** * **场景:** 构建一个推荐系统，根据用户的历史行为、人口统计信息和当前上下文（例如，一天中的时间、位置）来推荐产品或内容。 * **如何使用 MCP:** * 将用户历史行为、人口统计信息和上下文信息编码为 MCP 格式。 * 将 MCP 数据传递给您的推荐模型（例如，在 Amazon SageMaker 上部署的模型）。 * 模型使用 MCP 数据来生成个性化推荐。 * **AWS 服务:** Amazon Personalize, Amazon SageMaker, Amazon DynamoDB (存储用户数据), Amazon Location Service (获取位置信息) * **示例代码 (伪代码):** ```python # 假设我们已经从 DynamoDB 获取了用户数据，并从 Location Service 获取了位置信息 user_id = "user123" user_data = get_user_data_from_dynamodb(user_id) location_data = get_location_data_from_location_service(user_id) # 构建 MCP 数据 mcp_data = { "user_id": user_id, "user_history": user_data["purchase_history"], "user_age": user_data["age"], "location": location_data["city"], "time_of_day": "evening" } # 将 MCP 数据传递给 SageMaker 模型 response = sagemaker_endpoint.invoke(mcp_data) # 从响应中获取推荐结果 recommendations = response["recommendations"] ``` * **欺诈检测:** * **场景:** 检测金融交易中的欺诈行为，考虑交易金额、交易地点、用户行为模式等上下文信息。 * **如何使用 MCP:** * 将交易金额、交易地点、用户行为模式等信息编码为 MCP 格式。 * 将 MCP 数据传递给您的欺诈检测模型。 * 模型使用 MCP 数据来评估交易的欺诈风险。 * **AWS 服务:** Amazon Fraud Detector, Amazon SageMaker, Amazon Kinesis (实时数据流), Amazon DynamoDB (存储用户行为数据) * **示例代码 (伪代码):** ```python # 假设我们已经从 Kinesis 获取了实时交易数据 transaction_data = get_transaction_data_from_kinesis() # 构建 MCP 数据 mcp_data = { "transaction_amount": transaction_data["amount"], "transaction_location": transaction_data["location"], "user_behavior": get_user_behavior_from_dynamodb(transaction_data["user_id"]) } # 将 MCP 数据传递给 Fraud Detector 或 SageMaker 模型 fraud_score = fraud_detector.detect_fraud(mcp_data) # 或 sagemaker_endpoint.invoke(mcp_data) # 根据欺诈评分采取行动 if fraud_score > threshold: flag_transaction_as_fraudulent() ``` * **自然语言处理 (NLP):** * **场景:** 提高文本分类、情感分析或机器翻译等 NLP 任务的准确性，通过提供文档的上下文信息（例如，作者、来源、主题）。 * **如何使用 MCP:** * 将文档内容、作者、来源、主题等信息编码为 MCP 格式。 * 将 MCP 数据传递给您的 NLP 模型。 * 模型使用 MCP 数据来更好地理解文本并提高性能。 * **AWS 服务:** Amazon Comprehend, Amazon SageMaker, Amazon S3 (存储文档), Amazon Kendra (企业搜索) * **示例代码 (伪代码):** ```python # 假设我们已经从 S3 获取了文档 document = get_document_from_s3("s3://my-bucket/my-document.txt") # 构建 MCP 数据 mcp_data = { "document_content": document, "document_author": "John Doe", "document_source": "News Article", "document_topic": "Politics" } # 将 MCP 数据传递给 Comprehend 或 SageMaker 模型 sentiment = comprehend.detect_sentiment(mcp_data) # 或 sagemaker_endpoint.invoke(mcp_data) # 分析情感 print(f"Sentiment: {sentiment}") ``` * **图像识别:** * **场景:** 提高图像识别的准确性，通过提供图像的上下文信息（例如，拍摄地点、时间、天气）。 * **如何使用 MCP:** * 将图像数据、拍摄地点、时间、天气等信息编码为 MCP 格式。 * 将 MCP 数据传递给您的图像识别模型。 * 模型使用 MCP 数据来更好地识别图像中的对象。 * **AWS 服务:** Amazon Rekognition, Amazon SageMaker, Amazon S3 (存储图像), Amazon Location Service (获取位置信息) * **示例代码 (伪代码):** ```python # 假设我们已经从 S3 获取了图像 image = get_image_from_s3("s3://my-bucket/my-image.jpg") # 获取图像的元数据 (例如，通过 EXIF 数据) image_metadata = get_image_metadata(image) # 构建 MCP 数据 mcp_data = { "image_data": image, "location": image_metadata["location"], "time": image_metadata["time"], "weather": get_weather_data_from_location_service(image_metadata["location"]) } # 将 MCP 数据传递给 Rekognition 或 SageMaker 模型 objects = rekognition.detect_objects(mcp_data) # 或 sagemaker_endpoint.invoke(mcp_data) # 识别图像中的对象 print(f"Objects detected: {objects}") ``` **关键考虑因素:** * **数据格式:** 定义清晰的 MCP 数据格式，以便模型能够正确解析和使用上下文信息。 可以使用 JSON 或其他结构化数据格式。 * **数据量:** 考虑 MCP 数据的大小和复杂性，以及它对模型性能的影响。 优化数据传输和处理流程。 * **安全性:** 确保 MCP 数据的安全性，特别是当包含敏感信息时。 使用 AWS Identity and Access Management (IAM) 控制访问权限。 * **模型训练:** 在训练模型时，使用包含上下文信息的 MCP 数据，以便模型能够学习如何利用这些信息。 * **模型部署:** 确保模型部署环境能够接收和处理 MCP 数据。 可以使用 Amazon SageMaker Endpoints 或其他部署选项。 * **监控:** 监控模型的性能，并根据需要调整 MCP 数据或模型参数。 使用 Amazon CloudWatch 监控指标。 **总结:** 模型上下文协议 (MCP) 是一种强大的工具，可以提高机器学习模型的性能和可解释性。 通过将 MCP 与 AWS 服务集成，您可以构建更智能、更个性化的应用程序。 以上示例展示了 MCP 在不同场景下的应用，并提供了使用 AWS 服务的指导。 请根据您的具体需求进行调整和扩展。 希望这些示例对您有所帮助！ 如果您有任何其他问题，请随时提出。

COTI MCP Server

Enables AI applications to interact with the COTI blockchain for private token operations, supporting account management, private ERC20/ERC721 tokens, and secure transactions using Multi-Party Computation (MPC) technology.

MCP Adobe Experience Platform Server

A Node.js server that provides a comprehensive API interface for Adobe Experience Platform (AEP) integration. It enables users to manage schemas, datasets, segments, and profiles while supporting data ingestion and query services.

MCP Adapter

Automatically converts OpenAPI specifications into Model Context Protocol applications, enabling HTTP APIs to be managed as MCP services. It features a dynamic architecture that monitors file systems or Kubernetes ConfigMaps to update MCP tools in real-time.

Dune Analytics MCP Server

A Model Context Protocol server that connects AI agents to Dune Analytics data, providing access to DEX metrics, EigenLayer statistics, and Solana token balances through structured tools.

MCP Files

Enables agents to quickly find and edit code in a codebase with surgical precision. Find symbols, edit them everywhere with tools for reading code blocks, searching/replacing text, and making precise line-based modifications.

MCP Agentic RAG

Enables querying a vector database of machine learning FAQs and performing web searches using Bright Data proxies through Claude Desktop with structured agent prompts.

ethereum-validator-queue-mcp

An MCP server that tracks Ethereum’s validator activation and exit queues in real time, enabling AI agents to monitor staking dynamics and network participation trends.

MySQL MCP Server

A lightweight MySQL MCP server that enables LLMs to interact with databases through tools for schema inspection and query execution. It features LLM-friendly formatting, SSL support, and a secure read-only mode with query timeout protections.

insights-mcp-server

红帽 Insights MCP 服务器 POC (Hóngmào Insights MCP fúwùqì POC) This translates to: * **红帽 (Hóngmào):** Red Hat * **Insights:** Insights (The English word is often used directly in Chinese in technical contexts) * **MCP:** MCP (The English abbreviation is often used directly in Chinese in technical contexts) * **服务器 (fúwùqì):** Server * **POC:** POC (Proof of Concept - The English abbreviation is often used directly in Chinese in technical contexts) Therefore, a more natural translation, especially in a technical setting, might be: **红帽 Insights MCP 服务器概念验证 (Hóngmào Insights MCP fúwùqì gàiniàn yànzhèng)** Where: * **概念验证 (gàiniàn yànzhèng):** Concept Validation (This is a more formal translation of "Proof of Concept") While the first translation is perfectly understandable, the second one is more precise and commonly used in formal documentation. Choose the one that best suits your audience and the context.

Google Calendar Meeting Setup

Enables creating Google Calendar meeting invites with automated authentication via OAuth. Supports scheduling meetings with customizable title, duration, notes, and attendees through a simple command-line or MCP tool interface.

Theneo MCP Server

Enables AI assistants to automatically create, update, and publish API documentation through Theneo's platform. Supports OpenAPI specs, Postman collections, AI-powered description generation, and natural language interactions for seamless documentation workflows.

BlenderMCP

Connects Blender to Claude AI, enabling AI-assisted 3D modeling, scene creation, object manipulation, material control, and code execution directly in Blender through natural language prompts.

LumenX-MCP Legal Spend Intelligence Server

MCP server that enables intelligent analysis of legal spend data across multiple sources (LegalTracker, databases, CSV/Excel files), providing features like spend summaries, vendor performance analysis, and budget comparisons.

Bitbucket MCP Server

Enables AI assistants to interact with Bitbucket Cloud repositories, allowing users to manage pull requests, comments, tasks, and branches through natural language commands.

Fact Check Tools MCP Server

An MCP server that enables interaction with Google's Fact Check Tools API, allowing users to query and manage fact-check claims and publishers through natural language commands.

iCloud CalDAV MCP Connector

An MCP server that enables interaction with iCloud Calendars via the CalDAV protocol using an app-specific password. It allows users to list, create, update, and delete calendar events through MCP-aware clients like ChatGPT.

MCP Seekr Server

Enables web search through Google and Wikipedia plus content extraction from any webpage via the Seekr API. Provides real-time search results with advanced filtering options and clean text extraction capabilities.

E0N-MCP-FOR-ETH

just a simple test for education

EasyPeasyMCP

A lightweight, zero-config MCP server that makes documentation and API specifications instantly accessible to AI models using the llms.txt standard. It enables searching and retrieving full documentation, OpenAPI, and AsyncAPI specs without requiring a complex RAG infrastructure or vector database.

Chess MCP Server

Enables chess gameplay and interaction through MCP protocol. Allows users to play chess games, make moves, and manage chess sessions through natural language commands.

Next.js MCP Server

A template MCP server built with Next.js using the Vercel MCP Adapter. Provides a framework for deploying MCP servers with custom tools, prompts, and resources on Vercel with SSE transport support.

Mcp Server Amq

用于与 AWS AmazonMQ API 交互的 MCP 服务器