发现优秀的 MCP 服务器

MCP Data Server

An MCP server that connects AI agents to cloud-native geospatial data via STAC metadata and DuckDB with H3 spatial indexing, enabling zero-configuration SQL queries on terabyte-scale datasets over S3.

PubMed MCP Server

Enables AI agents to search and read PubMed academic literature directly, without requiring an NCBI API key.

UNWIND

A security layer for AI agents that monitors, checks, and records every action, providing a trust light, timeline, rewind, and ghost mode for testing without consequences.

ACC.MCP

An MCP server that exposes Autodesk Platform Services (APS) as tools for AI assistants to interact with the APS Data Management API. It enables users to authenticate and manage hubs, projects, and folders through a standardized interface.

mcp-google-gdrive

An MCP server that enables AI assistants to list, read, create, and manage files in Google Drive. It features automatic conversion of Google Workspace formats into Markdown, CSV, and plain text for seamless integration with AI workflows.

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。

Design Style MCP Server

Provides deterministic design style recommendations and structured tokens for AI content generation, with 30 curated styles including color palettes, typography, and visual directives.

authorize-net-mcp

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

LuzzyTool

A local smart tool collection based on MCP that enables AI assistants to perform file management, document editing, shell execution, semantic retrieval, and text processing.

Arco Lexicon

Canonical vocabulary server for autonomous business design. Exposes the Arco Lexicon as seven MCP tools: term lookup, related terms, alignment verification, citation formatting, source retrieval, term listing, and term suggestion. No authentication required. Streamable HTTP transport.

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!

worldmonitor-mcp

MCP server wrapping worldmonitor global intelligence dashboard, exposing 140 tools across 32 services for live market, geopolitical, military, cyber, climate, and supply chain data.

cloudprice-mcp

MCP server that lets Claude (or any MCP-compatible client) compare on-demand compute + storage pricing across AWS, Azure, and GCP in real time.

Financial Data MCP Server

Provides real-time and historical financial market data from Yahoo Finance, including stock prices, options chains, and company news. It also enables technical analysis calculations like moving averages and RSI for comparing or analyzing stock performance.

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)

ASCIIFlow MCP Server

Exposes ASCIIFlow drawing primitives as tools to enable AI assistants to generate ASCII wireframes and diagrams from natural language descriptions. It provides commands for creating canvases and drawing boxes, lines, arrows, and text using a character-grid coordinate system.

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。

Moleculer MCP Server

Exposes Moleculer microservices as MCP tools for AI assistants like Claude and GPT, with auto-discovery and schema conversion.

DBeast

DBeast MCP is a robust, security-first Model Context Protocol server designed to empower AI coding assistants (like Claude Desktop) with deep PostgreSQL database insights. Instead of just running basic SQL queries, DBeast equips your AI agent with tools to inspect active locks, audit slow queries, analyze table schemas, and safely generate index recommendations in real-time. Built specifically for

Claude Integration with MCP for Microsoft Graph API

Claude 集成 MCP 服务器以用于 Microsoft Graph API

mcp-knowledgebase

Enables AI agents to explore MySQL database schemas and execute read-only queries through a safe, MCP interface.

multimodal-proxy

为纯文本主模型提供多模态能力的MCP服务器，通过外包图像、视频和音频分析给外部多模态模型并回填文字结果。

Grabba MCP Server

Microservice Connector Protocol server that exposes Grabba API functionalities as callable tools, allowing AI agents and applications to interact with Grabba's data extraction and management services.

gsc-mcp-server

Enables Claude to access Google Search Console data including search performance, URL indexation, sitemaps, and built-in SEO analysis tools such as trending queries, cannibalization detection, and traffic drop diagnostics.

MCPDocSearch

Crawls documentation websites and provides semantic search capabilities over the content through vector embeddings, enabling natural language queries of technical documentation.

ICP Intelligence MCP

Enables deep ICP analysis with 9 tools for ideal customer profiling, market sizing, buyer mapping, and account prioritization.

gl-mcp-feedback

A feedback-oriented MCP server with a Web UI for AI development workflows, enabling interactive confirmations and reducing speculative tool calls.

ContextOS MCP Server

Unified context intelligence layer for AI agents, enabling orchestration of memory, reasoning, and self-healing indexes with cognition primitives and churn-aware retrieval routing.

EDCB MCP Server

Enables LLMs to control EDCB, a broadcast recording software, allowing program information retrieval and reservation management.

git-slim

A token-optimized Git MCP server that reduces context window tokens by 59% while preserving full functionality, enabling AI assistants to interact with Git repositories using only 5 grouped tool operations.