发现优秀的 MCP 服务器

Lucidity MCP

一个模型上下文协议服务器，通过对10个关键维度进行全面分析，从而提高人工智能生成的代码质量，帮助在问题出现之前识别它们。

MCP Context Manager

使用知识图谱提高效率的 MCP 服务器，用于在工作会话中保持持久上下文。

MCP Test

带有 GitHub 集成的 MCP 服务器 (Dài yǒu GitHub jíchéng de MCP fúwùqì)

focus_mcp_sql

一个基于 FocusSearch 关键词解析的 NL2SQL 插件，提供更高的准确率、更快的速度和更强的可靠性！

LLM Gateway MCP Server

一个 MCP 原生服务器，能够实现从 Claude 等高级 AI 代理到更具成本效益的 LLM 的智能任务委派，在优化成本的同时保持输出质量。

LumiFAI MCP Technical Analysis Server

好的，这是将英文翻译成中文： Provides technical analysis tools for cryptocurrency trading data, calculating EMAs (12 and 26 periods) for Binance pairs using MongoDB for data storage. **中文翻译：** 提供加密货币交易数据的技术分析工具，使用 MongoDB 存储数据，并计算币安交易对的 EMA（12 期和 26 期）。 **更详细的翻译，可以根据上下文进行调整：** * **更侧重功能描述：** 提供加密货币交易数据的技术分析工具，该工具使用 MongoDB 存储数据，并计算币安交易对的 12 期和 26 期指数移动平均线 (EMA)。 * **更侧重技术实现：** 构建用于加密货币交易数据的技术分析工具，该工具利用 MongoDB 存储数据，并计算币安交易对的 12 期和 26 期 EMA。 选择哪个翻译版本取决于你希望强调的重点。

Azure DevOps MCP Server for Cline

用于 Azure DevOps 集成的模型上下文协议服务器

MCP Image Recognition Server

提供图像识别功能，使用 Anthropic Claude Vision 和 OpenAI GPT-4 Vision API，支持多种图像格式，并提供通过 Tesseract OCR 进行可选的文本提取。

RhinoMCP

通过模型上下文协议将 Rhino3D 连接到 Claude AI，从而通过直接控制 Rhino 的功能实现 AI 辅助的 3D 建模和设计工作流程。

Mcp Qdrant Docker

Okay, here's a breakdown of Docker configuration for a Qdrant MCP (Multi-Cluster Proxy) server, along with explanations and best practices. I'll provide a `docker-compose.yml` example and discuss the key elements. **Understanding Qdrant MCP** The Qdrant MCP acts as a gateway to multiple Qdrant clusters. It handles routing requests to the appropriate cluster based on configuration. This is useful for: * **Scaling:** Distributing your data across multiple Qdrant clusters. * **Isolation:** Separating data for different tenants or applications. * **High Availability:** Routing around failed clusters. * **Geo-Distribution:** Placing data closer to users. **`docker-compose.yml` Example** ```yaml version: "3.9" services: qdrant-mcp: image: qdrant/qdrant-mcp:latest # Or specify a version ports: - "6333:6333" # MCP API port - "6334:6334" # MCP GRPC port environment: QDRANT_MCP_CONFIG_PATH: /qdrant-mcp/config/config.yaml # Path to your config file volumes: - ./config:/qdrant-mcp/config # Mount your config directory restart: unless-stopped depends_on: - qdrant-cluster-1 # Replace with your actual cluster service names - qdrant-cluster-2 # Replace with your actual cluster service names # Add more dependencies as needed qdrant-cluster-1: image: qdrant/qdrant:latest # Or specify a version ports: - "6335:6333" # Cluster 1 API port environment: QDRANT__SERVICE__GRPC_PORT: 6336 volumes: - qdrant_data_1:/qdrant/storage restart: unless-stopped qdrant-cluster-2: image: qdrant/qdrant:latest # Or specify a version ports: - "6337:6333" # Cluster 2 API port environment: QDRANT__SERVICE__GRPC_PORT: 6338 volumes: - qdrant_data_2:/qdrant/storage restart: unless-stopped volumes: qdrant_data_1: qdrant_data_2: ``` **Explanation:** 1. **`version: "3.9"`:** Specifies the Docker Compose file version. Use a version compatible with your Docker installation. 2. **`services:`:** Defines the services (containers) that will be run. 3. **`qdrant-mcp:`:** The service for the Qdrant MCP. * **`image: qdrant/qdrant-mcp:latest`:** Uses the official Qdrant MCP Docker image. **Important:** Consider using a specific version tag (e.g., `qdrant/qdrant-mcp:v1.5.0`) instead of `latest` for production to ensure consistent behavior. * **`ports:`:** Maps the container ports to the host ports. * `6333:6333`: The main Qdrant MCP API port (HTTP). You'll use this to send requests to the MCP. * `6334:6334`: The Qdrant MCP gRPC port. * **`environment:`:** Sets environment variables for the container. * `QDRANT_MCP_CONFIG_PATH: /qdrant-mcp/config/config.yaml`: Specifies the path to the MCP configuration file *inside* the container. This is crucial. * **`volumes:`:** Mounts a directory from your host machine into the container. * `./config:/qdrant-mcp/config`: Mounts the `./config` directory on your host to `/qdrant-mcp/config` inside the container. This is where you'll place your `config.yaml` file. **You MUST create this `./config` directory and put your `config.yaml` file in it.** * **`restart: unless-stopped`:** Automatically restarts the container if it crashes, unless you explicitly stop it. Good for reliability. * **`depends_on:`:** Specifies dependencies. The MCP will only start *after* the listed services (your Qdrant clusters) are running. **Important:** Replace `qdrant-cluster-1` and `qdrant-cluster-2` with the actual names of your Qdrant cluster services in your `docker-compose.yml`. Add more as needed. 4. **`qdrant-cluster-1` and `qdrant-cluster-2`:** Example Qdrant cluster services. You'll need to configure these according to your needs. * **`image: qdrant/qdrant:latest`:** Uses the official Qdrant Docker image. Again, use a specific version tag for production. * **`ports:`:** Maps the container ports to the host ports. Make sure these ports don't conflict with each other or with the MCP. * **`environment:`:** Sets environment variables for the container. The `QDRANT__SERVICE__GRPC_PORT` is important for internal communication within the cluster. * **`volumes:`:** Mounts a volume for persistent storage of the Qdrant data. `qdrant_data_1:/qdrant/storage` creates a named volume. * **`restart: unless-stopped`:** Automatically restarts the container if it crashes. 5. **`volumes:`:** Defines named volumes for persistent storage. This is important so your data isn't lost when the containers are stopped or restarted. **`config.yaml` (Qdrant MCP Configuration)** This is the most important part. The `config.yaml` file tells the MCP how to route requests to your Qdrant clusters. Here's an example: ```yaml clusters: cluster1: address: "qdrant-cluster-1:6333" # Use the service name and port cluster2: address: "qdrant-cluster-2:6333" # Use the service name and port collection_mappings: my_collection: cluster: cluster1 # All requests for "my_collection" go to cluster1 another_collection: cluster: cluster2 # All requests for "another_collection" go to cluster2 shared_collection: cluster: cluster1 # All requests for "shared_collection" go to cluster1 ``` **Explanation of `config.yaml`:** * **`clusters:`:** Defines the Qdrant clusters that the MCP will route to. * `cluster1`, `cluster2`: Arbitrary names for your clusters. Use descriptive names. * `address`: The address of the Qdrant cluster. **Crucially, use the Docker service name (e.g., `qdrant-cluster-1`) and the internal port (6333 by default).** Docker's internal DNS will resolve the service name to the container's IP address. Do *not* use `localhost` or the host's IP address here. * **`collection_mappings:`:** Defines how collections are mapped to clusters. * `my_collection`, `another_collection`, `shared_collection`: The names of your Qdrant collections. * `cluster`: The name of the cluster (as defined in the `clusters` section) that should handle requests for this collection. **Important Considerations and Best Practices:** * **Version Pinning:** Always use specific version tags for your Docker images (e.g., `qdrant/qdrant-mcp:v1.5.0`, `qdrant/qdrant:v1.5.0`) instead of `latest` in production. This prevents unexpected behavior when the images are updated. * **Configuration Management:** Use a proper configuration management system (e.g., environment variables, configuration files) to manage your Qdrant and MCP settings. Avoid hardcoding values in your Dockerfiles or Compose files. * **Networking:** Docker Compose automatically creates a default network for your services. This allows the services to communicate with each other using their service names. If you need more complex networking, you can define custom networks. * **Health Checks:** Implement health checks for your Qdrant clusters and the MCP. This allows Docker to automatically restart unhealthy containers. See the Qdrant documentation for details on health check endpoints. * **Logging:** Configure logging for your Qdrant clusters and the MCP. This is essential for troubleshooting. Docker can collect logs from the containers and send them to a central logging system. * **Monitoring:** Monitor the performance of your Qdrant clusters and the MCP. Use metrics to track CPU usage, memory usage, disk I/O, and network traffic. * **Security:** Secure your Qdrant clusters and the MCP. Use authentication and authorization to control access to your data. Consider using TLS/SSL to encrypt communication between the MCP and the clusters. * **Resource Limits:** Set resource limits (CPU, memory) for your containers to prevent them from consuming too many resources. * **Backup and Restore:** Implement a backup and restore strategy for your Qdrant data. * **Testing:** Thoroughly test your Qdrant MCP setup before deploying it to production. * **Qdrant Documentation:** Refer to the official Qdrant documentation for the most up-to-date information and best practices: [https://qdrant.tech/documentation/](https://qdrant.tech/documentation/) **How to Run:** 1. **Create the `config` directory:** `mkdir config` 2. **Create the `config.yaml` file:** Place the `config.yaml` file (with your cluster definitions and collection mappings) in the `config` directory. 3. **Save the `docker-compose.yml` file:** Save the `docker-compose.yml` file in the same directory as the `config` directory. 4. **Run Docker Compose:** `docker-compose up -d` (This will start the containers in detached mode.) **Troubleshooting:** * **Check the logs:** Use `docker-compose logs qdrant-mcp` (or the name of your MCP service) to view the logs for the MCP container. Look for errors related to configuration, cluster connections, or routing. * **Verify the configuration:** Double-check the `config.yaml` file for errors. Make sure the cluster addresses are correct and that the collection mappings are accurate. * **Check network connectivity:** Make sure the MCP container can communicate with the Qdrant cluster containers. You can use `docker exec -it qdrant-mcp bash` to enter the MCP container and then use tools like `ping` or `telnet` to test connectivity. * **Qdrant Cluster Status:** Ensure your Qdrant clusters are running and healthy *before* starting the MCP. **Chinese Translation of Key Terms:** * **Qdrant MCP (Multi-Cluster Proxy):** Qdrant 多集群代理 (Duō jíqún dàilǐ) * **Cluster:** 集群 (Jíqun) * **Collection:** 集合 (Jíhé) * **Configuration:** 配置 (Pèizhì) * **Docker Compose:** Docker Compose * **Service:** 服务 (Fúwù) * **Image:** 镜像 (Jìngxiàng) * **Container:** 容器 (Róngqì) * **Port:** 端口 (Duānkǒu) * **Environment Variable:** 环境变量 (Huánjìng biànliàng) * **Volume:** 卷 (Juǎn) * **Mapping:** 映射 (Yìngshè) This comprehensive guide should help you set up a Qdrant MCP server using Docker. Remember to adapt the configuration to your specific needs and environment. Good luck!

AGE-MCP-Server

一个 MCP 服务器，通过 Claude AI 提供与 Apache AGE 图数据库的自然语言交互，允许用户在 PostgreSQL 中查询、可视化和操作图数据。

🗄️ Couchbase MCP Server for LLMs

镜子 (jìng zi)

Perplexity Sonar MCP Server

用于将 Perplexity API 集成到 Claude Desktop 和其他 MCP 客户端的 MCP 服务器

PyMCPAutoGUI

一个 MCP 服务器，它将 AI 代理与 GUI 自动化功能桥接起来，使它们能够控制鼠标、键盘、窗口并截取屏幕截图，从而与桌面应用程序进行交互。

Claude Integration with MCP for Microsoft Graph API

Claude 集成 MCP 服务器以用于 Microsoft Graph API

OpenAI Image Generation MCP Server

Provides tools for generating and editing images using OpenAI's gpt-image-1 model via an MCP interface, enabling AI assistants to create and modify images based on text prompts.

Agile Luminary MCP Server

A Model Context Protocol server that connects AI clients to the Agile Luminary project management system, allowing users to retrieve project details, work assignments, and product information within AI conversations.

authorize-net-mcp

实验性的 Authorize.net Node.js TypeScript MCP 服务器

MCP File Server

用于读取和写入本地文件的 MCP 服务器 (Yòng yú dúqǔ hé xiě rù běndì wénjiàn de MCP fúwùqì) Alternatively, depending on the specific context, you might also say: 本地文件读写 MCP 服务器 (Běndì wénjiàn dú xiě MCP fúwùqì)

Gerrit Review MCP Server

Provides integration with Gerrit code review system, allowing AI assistants to fetch change details and compare patchset differences for code reviews.

Serveur MCP Airbnb

镜子 (jìng zi)

Kuzu MCP server

这个服务器支持用户和他们的Kuzu数据库之间进行自然语言交互，用户可以使用像Claude Desktop或Cursor这样的客户端，允许大型语言模型（LLM）检索数据库模式、执行Cypher查询、创建节点以及在图数据库中建立关系。

Run Model Context Protocol (MCP) servers with AWS Lambda

在 AWS Lambda 函数中运行现有的基于标准输入/输出 (stdio) 的模型上下文协议 (MCP) 服务器

GKE Hub API MCP Server

An auto-generated MCP server that enables interaction with Google Kubernetes Engine Hub API for multi-cluster management through natural language commands.

Atomistic Toolkit MCP Server

一个兼容 MCP 的服务器，通过 ASE、pymatgen 等提供原子模拟功能。