Discover Awesome MCP Servers

LSD MCP Server

An MCP server that connects language models to the LSD database, enabling web data extraction, research capabilities, and custom 'trips' for extending functionality through the internetdata SDK.

MCP Gemini Server

GoogleのGemini AIモデルをModel Context Protocol (MCP) インターフェースでラップした専用サーバー。他のLLMやMCP互換システムが、標準化されたツールを通じて、コンテンツ生成、関数呼び出し、チャット、ファイル処理といったGeminiの機能にアクセスできるようにする。

Register UZ MCP Server

Enables access to Slovak Registry of Financial Statements data, allowing users to search companies, retrieve financial reports, balance sheets, income statements, and analyze Slovak business financial data through natural language queries.

Storyden

A modern community hub with forum and knowledge base. Native MCP built-in, ready for the next era of internet culture.

Coolify MCP Server

Enables AI assistants to interact with Coolify self-hosted instances for application deployment, management, and monitoring. Features 4 unified tools optimized for VS Code's limits, covering app management, environment configuration, system administration, and built-in documentation.

MCP Server Resend

ClaudeがResend APIを通じてメールを作成・送信できるツール連携機能。配信予約やファイル添付などの機能に対応。

yfinance-mcp

「yfinance」用のPython MCPサーバー

kubernetes-mcp-server

A powerful and flexible Kubernetes MCP server implementation with support for OpenShift.

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

AIエージェントとGUI自動化機能を連携させ、マウス、キーボード、ウィンドウの操作、スクリーンショットの取得を通じてデスクトップアプリケーションと対話できるMCPサーバー。

ClickUp MCP Server

AIエージェントがClickUpワークスペースと連携し、自然言語コマンドを通じてタスクの作成、管理、ワークスペースの整理を可能にする、モデルコンテキストプロトコルサーバー。

OSP Marketing Tools MCP Server

Open Strategy Partnersの手法に基づいたマーケティングツールを提供する、Model Context ProtocolサーバーのTypeScript実装。価値マップの生成、メタ情報作成、コンテンツ編集などのツールを通じて、コンテンツの作成、最適化、製品ポジショニングを可能にします。

Random Value MCP Server

Generates random integers within a specified range and random alphanumeric strings of specified length through MCP protocol integration.

Walrus MCP Server

Enables AI assistants to store and retrieve data using the Walrus decentralized storage network. Supports blob storage operations, retrieval by ID, and blockchain-verified availability through the Sui network.

Big Brain MCP

Mantleネットワークの主要プロトコルから統計情報を取得し、ユーザーがより多くの情報に基づいて投資判断を下せるようにするMCPサーバー。

Code Execution Server

Enables execution of code in a sandbox environment with integrated web search capabilities. Provides a basic framework for running code safely, primarily designed for AI agents and research applications.

DICOM-MCP

DICOM医用画像を、シンプルなノートストレージシステムを通して扱えるようにする、モデルコンテキストプロトコルサーバー。

Nmap MCP Server

Enables network scanning and security assessment using Nmap through MCP, allowing AI assistants to perform port scans, service detection, and network reconnaissance on specified targets with configurable scan parameters.

Last9 Observability MCP

リアルタイムの本番環境のコンテキスト（ログ、メトリクス、トレース）をローカル環境にシームレスに取り込み、コードの自動修正を迅速化します。

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 cover the key aspects: **1. Dockerfile (Example)** This is the foundation. It defines how your Docker image is built. ```dockerfile # Use an official Qdrant image as the base FROM qdrant/qdrant:latest # Or a specific version, e.g., qdrant/qdrant:v1.7.5 # Set environment variables (optional, but recommended) ENV QDRANT__SERVICE__GRPC_PORT=6333 ENV QDRANT__SERVICE__HTTP_PORT=6332 ENV QDRANT__CLUSTER__NODE_ID=mcp-node-1 # Unique ID for this MCP instance ENV QDRANT__CLUSTER__TYPE=mcp ENV QDRANT__CLUSTER__MCP__CLUSTER_URIS="qdrant-node-1:6333,qdrant-node-2:6333" # Comma-separated list of Qdrant cluster nodes ENV QDRANT__CLUSTER__MCP__REPLICATION_FACTOR=2 # Replication factor for the MCP ENV QDRANT__STORAGE__STORAGE_PATH=/qdrant/storage # Path for storage (important for persistence) # Expose ports (important for access) EXPOSE 6333 # gRPC port EXPOSE 6332 # HTTP port # Healthcheck (optional, but highly recommended) HEALTHCHECK --interval=30s --timeout=10s --retries=3 \ CMD curl -f http://localhost:6332/healthz || exit 1 # Command to start Qdrant (usually not needed, but can be customized) # CMD ["/usr/local/bin/qdrant", "--config-path", "/path/to/your/config.yaml"] # If you have a custom config file ``` **Explanation of Dockerfile Instructions:** * **`FROM qdrant/qdrant:latest`**: This line is crucial. It pulls the official Qdrant Docker image from Docker Hub. Using `latest` is convenient for quick testing, but **strongly consider using a specific version tag** (e.g., `qdrant/qdrant:v1.7.5`) for production to ensure consistent behavior and avoid unexpected updates. * **`ENV ...`**: Environment variables are the preferred way to configure Qdrant within Docker. Here's a breakdown of the key variables for an MCP setup: * `QDRANT__SERVICE__GRPC_PORT`: The gRPC port Qdrant will listen on. Defaults to 6333. * `QDRANT__SERVICE__HTTP_PORT`: The HTTP port Qdrant will listen on. Defaults to 6332. * `QDRANT__CLUSTER__NODE_ID`: A unique identifier for this Qdrant instance within the cluster. Make sure each node has a different ID. * `QDRANT__CLUSTER__TYPE=mcp`: Specifies that this instance is an MCP. * `QDRANT__CLUSTER__MCP__CLUSTER_URIS`: A comma-separated list of the gRPC addresses of the Qdrant nodes that this MCP will proxy to. The format is `hostname:port`. **Important:** These hostnames must be resolvable from within the Docker container. If your Qdrant nodes are running in the same Docker network, use their service names (e.g., `qdrant-node-1:6333`). If they are running on the host machine, you might need to use `host.docker.internal:6333` (on Docker Desktop) or configure your network accordingly. * `QDRANT__CLUSTER__MCP__REPLICATION_FACTOR`: The replication factor for the MCP. This determines how many underlying Qdrant nodes will store each data point. * `QDRANT__STORAGE__STORAGE_PATH`: The directory where Qdrant will store its data. **Very important for persistence!** You'll want to mount a Docker volume to this path to ensure that your data survives container restarts. * **`EXPOSE 6333 6332`**: This tells Docker that the container will listen on these ports. It's mostly for documentation and doesn't automatically publish the ports. You'll need to use the `-p` flag in `docker run` or the `ports` section in `docker-compose.yml` to actually map these ports to the host. * **`HEALTHCHECK`**: A health check is crucial for container orchestration. This command periodically checks if the Qdrant service is healthy by sending an HTTP request to the `/healthz` endpoint. If the request fails, the container is considered unhealthy, and your orchestrator (e.g., Docker Compose, Kubernetes) can take action (e.g., restart the container). * **`CMD ...`**: This specifies the command to run when the container starts. In most cases, you don't need to override the default command provided by the `qdrant/qdrant` image. However, if you have a custom configuration file, you can use this to specify its path. **2. Docker Compose (Example)** Docker Compose is a convenient way to define and manage multi-container Docker applications. Here's an example `docker-compose.yml` file for a Qdrant MCP setup with two Qdrant nodes: ```yaml version: "3.9" services: qdrant-node-1: image: qdrant/qdrant:latest container_name: qdrant-node-1 ports: - "6333:6333" - "6332:6332" environment: QDRANT__SERVICE__GRPC_PORT: 6333 QDRANT__SERVICE__HTTP_PORT: 6332 QDRANT__CLUSTER__NODE_ID: qdrant-node-1 QDRANT__STORAGE__STORAGE_PATH: /qdrant/storage volumes: - qdrant-node-1-data:/qdrant/storage networks: - qdrant-net qdrant-node-2: image: qdrant/qdrant:latest container_name: qdrant-node-2 ports: - "6334:6333" # Map host port 6334 to container port 6333 - "6335:6332" # Map host port 6335 to container port 6332 environment: QDRANT__SERVICE__GRPC_PORT: 6333 QDRANT__SERVICE__HTTP_PORT: 6332 QDRANT__CLUSTER__NODE_ID: qdrant-node-2 QDRANT__STORAGE__STORAGE_PATH: /qdrant/storage volumes: - qdrant-node-2-data:/qdrant/storage networks: - qdrant-net qdrant-mcp: image: qdrant/qdrant:latest container_name: qdrant-mcp ports: - "6336:6333" # Map host port 6336 to container port 6333 - "6337:6332" # Map host port 6337 to container port 6332 environment: QDRANT__SERVICE__GRPC_PORT: 6333 QDRANT__SERVICE__HTTP_PORT: 6332 QDRANT__CLUSTER__NODE_ID: qdrant-mcp QDRANT__CLUSTER__TYPE: mcp QDRANT__CLUSTER__MCP__CLUSTER_URIS: "qdrant-node-1:6333,qdrant-node-2:6333" QDRANT__CLUSTER__MCP__REPLICATION_FACTOR: 2 QDRANT__STORAGE__STORAGE_PATH: /qdrant/storage depends_on: - qdrant-node-1 - qdrant-node-2 networks: - qdrant-net volumes: qdrant-node-1-data: qdrant-node-2-data: networks: qdrant-net: driver: bridge ``` **Explanation of Docker Compose File:** * **`version: "3.9"`**: Specifies the Docker Compose file version. * **`services:`**: Defines the services (containers) that make up your application. * **`qdrant-node-1` and `qdrant-node-2`**: These are the two Qdrant nodes that the MCP will proxy to. * `image`: Uses the official Qdrant image. * `container_name`: Sets a name for the container. * `ports`: Maps ports from the host machine to the container. **Important:** In this example, I've mapped different host ports to the same container ports for each node. This is necessary because you can't have multiple containers binding to the same port on the host. Adjust these mappings as needed. * `environment`: Sets the environment variables for the Qdrant nodes. Note that each node has a unique `QDRANT__CLUSTER__NODE_ID`. * `volumes`: Mounts Docker volumes to the `/qdrant/storage` directory to persist data. * `networks`: Attaches the containers to a Docker network called `qdrant-net`. This allows the containers to communicate with each other using their service names as hostnames. * **`qdrant-mcp`**: This is the Qdrant MCP container. * `image`: Uses the official Qdrant image. * `container_name`: Sets a name for the container. * `ports`: Maps ports from the host machine to the container. * `environment`: Sets the environment variables for the Qdrant MCP. Crucially, `QDRANT__CLUSTER__TYPE` is set to `mcp`, and `QDRANT__CLUSTER__MCP__CLUSTER_URIS` points to the gRPC addresses of the Qdrant nodes. * `depends_on`: Ensures that the Qdrant nodes are started before the MCP. * `networks`: Attaches the container to the `qdrant-net` network. * **`volumes:`**: Defines the Docker volumes used for data persistence. * **`networks:`**: Defines the Docker network used for communication between the containers. **3. Building and Running** 1. **Create the Dockerfile and docker-compose.yml:** Save the code above into files named `Dockerfile` and `docker-compose.yml` in the same directory. 2. **Build the image (if needed):** If you've customized the Dockerfile, build the image: ```bash docker build -t qdrant-mcp . # The dot (.) means the current directory ``` 3. **Run with Docker Compose:** ```bash docker-compose up -d # -d runs in detached mode (background) ``` 4. **Check the logs:** ```bash docker-compose logs -f qdrant-mcp # Follow the logs of the MCP container docker-compose logs -f qdrant-node-1 docker-compose logs -f qdrant-node-2 ``` 5. **Stop the containers:** ```bash docker-compose down ``` **4. Important Considerations and Best Practices** * **Persistence:** The most critical aspect is data persistence. **Always mount Docker volumes to the `QDRANT__STORAGE__STORAGE_PATH` directory.** Without this, your data will be lost when the container is stopped or restarted. * **Networking:** Ensure that your Qdrant nodes and MCP can communicate with each other. Docker networks are the easiest way to achieve this. Use the service names as hostnames within the network. * **Resource Limits:** Consider setting resource limits (CPU, memory) for your containers using Docker Compose or Kubernetes. This prevents a single container from consuming all resources and potentially crashing the entire system. Example in `docker-compose.yml`: ```yaml services: qdrant-node-1: # ... other configurations deploy: resources: limits: cpus: '2' memory: 4G ``` * **Health Checks:** Implement health checks to ensure that your containers are running correctly. This allows your orchestrator to automatically restart unhealthy containers. * **Logging:** Configure proper logging for your Qdrant instances. Docker provides basic logging, but you might want to use a more advanced logging solution (e.g., ELK stack, Graylog) for production environments. * **Monitoring:** Monitor the performance of your Qdrant cluster using metrics. Qdrant exposes metrics in Prometheus format. You can use Prometheus and Grafana to visualize these metrics. * **Security:** * **Never expose your Qdrant ports directly to the internet without proper security measures.** Use a reverse proxy (e.g., Nginx, Traefik) with authentication and authorization. * Consider using TLS/SSL for communication between your Qdrant nodes and clients. * Regularly update your Qdrant images to the latest versions to patch security vulnerabilities. * **Configuration Management:** For complex deployments, consider using a configuration management tool (e.g., Ansible, Terraform) to automate the deployment and configuration of your Qdrant cluster. * **Kubernetes:** For production deployments, Kubernetes is highly recommended for its scalability, resilience, and management capabilities. There are Helm charts available for deploying Qdrant on Kubernetes. * **Version Pinning:** Always pin your Qdrant image versions (e.g., `qdrant/qdrant:v1.7.5`) in your Dockerfile and Docker Compose files. This ensures that you're using a consistent version of Qdrant and avoids unexpected behavior due to automatic updates. * **MCP Replication Factor:** The `QDRANT__CLUSTER__MCP__REPLICATION_FACTOR` determines how many underlying Qdrant nodes will store each data point. A higher replication factor provides better fault tolerance but also increases storage requirements. Choose a replication factor that is appropriate for your needs. * **Resource Allocation:** Carefully consider the resource requirements of your Qdrant nodes and MCP. Allocate enough CPU, memory, and storage to ensure that your cluster can handle your workload. Monitor resource usage and adjust as needed. **Japanese Translation of Key Phrases:** * Docker configuration: Docker の設定 (Docker no settei) * Qdrant MCP server: Qdrant MCP サーバー (Qdrant MCP sābā) * Dockerfile: Dockerfile (ドッカーファイル) * Docker Compose: Docker Compose (ドッカーコンポーズ) * Environment variables: 環境変数 (kankyō hensu) * Ports: ポート (pōto) * Volumes: ボリューム (boryūmu) * Networking: ネットワーク (nettowāku) * Persistence: 永続性 (eizokusei) * Health check: ヘルスチェック (herusu chekku) * Resource limits: リソース制限 (risōsu seigen) * Security: セキュリティ (sekyuriti) * Replication factor: レプリケーションファクター (repurikēshon fakutā) This comprehensive guide should help you configure a Qdrant MCP server using Docker. Remember to adapt the examples to your specific needs and environment. Good luck!

MCP Test

GitHub連携機能付きのMCPサーバー

Freshservice MCP server

Freshservice MCP server

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.

Mattermost MCP Server

Enables Claude to interact with Mattermost through search functionality, user management, channel operations, and team information retrieval. Supports message search by keywords/users/dates, thread viewing, and displays all timestamps in Korean Standard Time (KST).

Fastmail MCP Server

An unofficial MCP server that enables users to manage their Fastmail accounts through natural language interactions. It provides tools to query mailboxes, retrieve email content with advanced filtering, and send messages directly through the Fastmail API.

RAGDocs

モデルコンテキストプロトコル（MCP）サーバーは、ベクトルデータベース（Qdrant）を使用してドキュメントのセマンティック検索と検索を可能にします。このサーバーを使用すると、URLまたはローカルファイルからドキュメントを追加し、自然言語クエリを使用してそれらを検索できます。

Random Word By Api Ninjas

Provides access to API Ninjas Random Word API to generate random words by type (noun, verb, adjective, adverb) for creative writing, games, and language learning applications.

LumiFAI MCP Technical Analysis Server

暗号通貨取引データのテクニカル分析ツールを提供します。MongoDBを使用してデータストレージを行い、BinanceペアのEMA（12期間と26期間）を計算します。