Discover Awesome MCP Servers

MythxEngine

Enables AI to act as Game Master for tabletop RPGs with deterministic dice, five-tier outcomes, FitD-style stress, combat tracking, clocks, investigations, NPC relationships, and world generation via 134 tools over MCP.

Domainnameregistration.com MCP

Enables fast, token-efficient bulk domain name search for AI agents, checking up to 1,000 domains per request to see availability statuses.

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.

Rodev MCP Server

Connects AI agents to Rodev DeFi vaults, enabling deposits, withdrawals, and status queries via 9 MCP tools.

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.

Cymbal Catering MCP Server

Enables secure, natural language access to private Cloud SQL PostgreSQL data for catering CRM management, exposing database CRUD operations as MCP tools via Cloud Run.

MealPlanner MCP Multi-Agent

A multimodal meal planning agent that uses Gemini Vision to analyze fridge photos, suggest recipes, provide nutritional info, and generate daily meal plans.

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.

LibreLink Up (CGM)

Read-only MCP server for accessing glucose readings, charts, logbook, and statistics from LibreLink Up sensors, supporting any MCP client.

Codecov MCP Server

Enables querying Codecov coverage data including file, commit, repository, and pull request coverage.

Heista MCP

Decode any video ad, load brand information, and generate ad scripts from inside MCP-compatible clients like Claude and ChatGPT.

video_agent

Enables probing, ingesting, transcribing, and searching Bilibili videos with timestamped evidence and frame extraction, without reading browser cookies.

dingdawg-healthcare-agent

Enables HIPAA-aware healthcare workflow automation including patient intake, clinical summaries, compliance checking, and appointment scheduling via MCP tools.

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!

Coffee Company MCP Server

An MCP adapter that maps Coffee Company B2B HTTP APIs to MCP tools, allowing AI agents to query member information, benefits, coupons, and payment statuses. It enables seamless integration for AI assistants to manage coffee-related customer assets and loyalty details through natural language.

Power Platform Pipeline MCP Server

Exposes Power Platform Pipeline operations as MCP tools for Copilot Studio agents, enabling pipeline discovery, deployments, approvals, and configuration management.

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.

tome-mcp

Manages a research paper library, providing PDF ingestion, bibliography management, semantic search, figure tracking, and integration with Semantic Scholar.

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.

obolpay-x402-mcp

MCP server for a live x402 payment gateway on Base (USDC). Lets AI agents discover, preview for free, then pay per call — with prepaid gasless payments, signed receipts, and delta delivery.

Singapore Location Intelligence MCP

Provides comprehensive Singapore transport routing with real-time public transport data, weather-aware journey planning, postal code resolution, and Google Maps-quality turn-by-turn navigation across MRT, LRT, buses, and walking routes.

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。

ICP Intelligence MCP

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

appcrane-mcp

MCP connector for AppCrane that serves a bundled tool catalog for offline introspection and proxies tool calls to a self-hosted AppCrane instance for deploying and managing AI-built apps.

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.

gl-mcp-feedback

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

think-mcp

Provides structured thinking tools including mental models, design patterns, debugging approaches, decision frameworks, and multi-persona reasoning to enhance AI assistant problem-solving capabilities.

MongoDB That Works - MCP Server

Enables interaction with MongoDB databases through CRUD operations, aggregation, and schema discovery, with automatic field validation and ObjectId conversion.