Discover Awesome MCP Servers

cloudscraper-mcp

Enables AI agents to bypass Cloudflare protection and scrape web content, returning clean Markdown with smart chunking and file export.

@kaminari-ad/mcp

Lets AI agents launch scans, inspect results, manage campaigns and policies, and read alerts directly against your Kaminari Ad workspace via your API key, with 82 tools covering most of the API surface.

instinct

Self-learning memory for AI coding agents. Observes tool sequences, user preferences, and recurring fixes — auto-promotes high-confidence patterns into behavioral rules. 22 tools, 2 prompts, SQLite-backed, zero config.

Orbination AI Desktop Vision & Control

Native Windows MCP server that gives AI agents full desktop control. 45+ tools using UIAutomation + OCR with automatic dark theme enhancement instead of screenshots. Features batch action sequencing (run_sequence), window occlusion detection, menu navigation, PrintWindow capture, and embedded AI workflow instructions. Single .NET 8 executable — no Python, no Node, no Selenium.

Gazebo MCP Server

Enables AI assistants to control Gazebo robot simulations through ROS2, including spawning robots (TurtleBot3), manipulating environments, accessing sensor data (camera, LiDAR, IMU), and managing simulation state.

FastAPI MCP Server

A comprehensive MCP server providing 21+ tools for mathematical operations, string manipulation, file handling, utilities, and web requests via FastAPI and WebSocket.

GAS MCP Server Advanced

An enterprise-grade MCP server that integrates AI agents with Google Sheets and Apps Script using over 100 specialized tools for automation and data analysis. It features a self-healing architecture with advanced security and observability for production-ready workflows.

Jinike AI MCP Server

Connects Claude to email, calendar, and tasks via Jinike AI, enabling inbox reading, email sending, event creation, task management, and daily briefings through Gmail, Outlook, and Google Calendar integration.

NIS2 Compliance MCP

NIS2 Compliance - MCP server providing AI-powered tools and automation by MEOK AI Labs

Typesense MCP Server

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AgentDB

MCP server providing agent memory management with layered awakening, per-agent SQLite storage, and a dashboard for monitoring and administration.

DICOMweb MCP Server

An MCP server that exposes a DICOMweb-compliant DICOM archive to AI assistants. It lets any MCP-capable client search studies, series and instances, inspect metadata, read Structured and Encapsulated PDF Reports, and render image frames — all through natural language.

MkDocs Material MCP Server

Enables searching and retrieving documentation from MkDocs Material-powered sites.

RepoNova

RepoNova is an MCP server that builds a persistent knowledge graph of your codebase, enabling AI agents to query code structure, dependencies, and semantics through 11 specialized tools.

YouTube MCP Server

Provides AI assistants with comprehensive YouTube analytics and channel management capabilities, including channel performance, video analytics, audience insights, and content strategy tools.

mcpo-docker

Okay, here's an example Dockerfile and some accompanying explanation to help you create a Docker image for `mcpo` (assuming it's a command-line tool that exposes MCP servers as OpenAPI endpoints for OpenWebUI). I'll make some reasonable assumptions about how `mcpo` works, but you'll need to adapt this to your specific needs. **Dockerfile** ```dockerfile # Use a base image with Python (e.g., slim version for smaller size) FROM python:3.11-slim-bookworm AS builder # Set a working directory inside the container WORKDIR /app # Copy the mcpo requirements file (if you have one) COPY requirements.txt . # Install mcpo dependencies (if any) RUN pip install --no-cache-dir -r requirements.txt # Copy the mcpo source code COPY . . # --- Final Image --- FROM python:3.11-slim-bookworm # Set a working directory inside the container WORKDIR /app # Copy the mcpo executable from the builder stage COPY --from=builder /app . # Expose the port mcpo will listen on (adjust as needed) EXPOSE 8000 # Define the command to run mcpo when the container starts CMD ["python", "mcpo.py", "--host", "0.0.0.0", "--port", "8000"] ``` **Explanation:** 1. **`FROM python:3.11-slim-bookworm AS builder`**: * This line specifies the base image for the Docker image. We're using a Python 3.11 slim image based on Debian Bookworm. The `slim` version is smaller than the full Python image, which is good for reducing the image size. The `AS builder` part gives this stage a name, "builder," which we'll use later. 2. **`WORKDIR /app`**: * Sets the working directory inside the container to `/app`. All subsequent commands will be executed relative to this directory. 3. **`COPY requirements.txt .`**: * Copies the `requirements.txt` file (if you have one) from your local directory to the `/app` directory inside the container. This file should list all the Python packages that `mcpo` depends on. If you don't have a `requirements.txt` file, you can create one using `pip freeze > requirements.txt` in your local `mcpo` development environment. 4. **`RUN pip install --no-cache-dir -r requirements.txt`**: * Installs the Python packages listed in `requirements.txt`. The `--no-cache-dir` option prevents `pip` from caching downloaded packages, which helps reduce the image size. 5. **`COPY . .`**: * Copies all the files and directories from your current directory (where the Dockerfile is located) to the `/app` directory inside the container. This includes the `mcpo.py` script (or whatever the main `mcpo` executable is called), any configuration files, and other necessary files. 6. **`FROM python:3.11-slim-bookworm`**: * Starts a new stage in the Docker build. This is important for creating a smaller final image. We're using the same base image as before. 7. **`WORKDIR /app`**: * Sets the working directory for the new stage. 8. **`COPY --from=builder /app .`**: * This is the key to multi-stage builds. It copies the contents of the `/app` directory from the `builder` stage to the `/app` directory in the current stage. This means we're only copying the compiled code and dependencies, not the build tools or intermediate files. 9. **`EXPOSE 8000`**: * Declares that the container will listen on port 8000. This is just metadata; it doesn't actually publish the port. You'll need to use the `-p` option when running the container to map the container's port 8000 to a port on your host machine. Adjust the port number if `mcpo` uses a different port. 10. **`CMD ["python", "mcpo.py", "--host", "0.0.0.0", "--port", "8000"]`**: * Specifies the command to run when the container starts. This assumes that `mcpo` is a Python script named `mcpo.py`. The `--host 0.0.0.0` option tells `mcpo` to listen on all network interfaces, which is necessary for accessing it from outside the container. The `--port 8000` option tells `mcpo` to listen on port 8000. **You'll need to adjust this command to match the actual command-line arguments that `mcpo` requires.** For example, you might need to specify a configuration file or other options. **How to Build and Run the Image:** 1. **Save the Dockerfile:** Save the above code as a file named `Dockerfile` in the same directory as your `mcpo` source code and `requirements.txt` (if you have one). 2. **Build the Image:** Open a terminal in that directory and run the following command: ```bash docker build -t mcpo-image . ``` * `docker build`: The Docker command to build an image. * `-t mcpo-image`: Tags the image with the name `mcpo-image`. You can choose any name you like. * `.`: Specifies that the Dockerfile is in the current directory. 3. **Run the Container:** After the image is built, run it with the following command: ```bash docker run -d -p 8000:8000 mcpo-image ``` * `docker run`: The Docker command to run a container. * `-d`: Runs the container in detached mode (in the background). * `-p 8000:8000`: Maps port 8000 on your host machine to port 8000 inside the container. This allows you to access `mcpo` from your host machine. If `mcpo` uses a different port, adjust this accordingly. * `mcpo-image`: The name of the image to run. 4. **Access `mcpo`:** Once the container is running, you should be able to access the `mcpo` server in your web browser or using `curl` at `http://localhost:8000` (or whatever port you mapped). The exact URL will depend on how `mcpo` exposes its OpenAPI endpoint. You'll likely need to consult the `mcpo` documentation to determine the correct URL. **Important Considerations and Customization:** * **`mcpo` Command-Line Arguments:** The `CMD` instruction in the Dockerfile is crucial. Make sure you replace the example command with the correct command-line arguments for `mcpo`. This might include specifying a configuration file, API keys, or other options. * **Dependencies:** Ensure that your `requirements.txt` file includes all the necessary Python packages for `mcpo`. If you're missing dependencies, the container will likely fail to start. * **Port:** Adjust the `EXPOSE` and `-p` options to match the port that `mcpo` uses. * **Volumes:** If `mcpo` needs to access files on your host machine (e.g., configuration files, data files), you can use Docker volumes to mount directories from your host machine into the container. For example: ```bash docker run -d -p 8000:8000 -v /path/to/config:/app/config mcpo-image ``` This would mount the `/path/to/config` directory on your host machine to the `/app/config` directory inside the container. * **Environment Variables:** You can use environment variables to configure `mcpo` at runtime. For example: ```dockerfile ENV API_KEY=your_api_key CMD ["python", "mcpo.py", "--api-key", "$API_KEY"] ``` Then, when you run the container, you can set the `API_KEY` environment variable: ```bash docker run -d -p 8000:8000 -e API_KEY=another_api_key mcpo-image ``` * **Logging:** Consider how `mcpo` logs its output. You might want to configure logging to a file or to standard output so that you can easily monitor the container's activity. * **Security:** If `mcpo` handles sensitive data, be sure to take appropriate security measures, such as using HTTPS, restricting access to the container, and protecting API keys. * **OpenWebUI Integration:** This Dockerfile focuses on running `mcpo`. You'll need to configure OpenWebUI to connect to the `mcpo` server. This typically involves specifying the URL of the `mcpo` server in OpenWebUI's settings. This comprehensive example should give you a solid starting point for creating a Docker image for `mcpo`. Remember to adapt it to your specific needs and consult the `mcpo` documentation for more information.

Ceki

Hire specialists by the hour — search, schedule, and pay via MCP protocol.

Gear

An MCP server that enables AI assistants to directly run, inspect, modify, and debug Godot game development projects through 110+ tools covering scenes, scripts, resources, runtime debugging, and asset management.

Agent Directory MCP Server

Enables AI agents to discover, register, and rate services in a decentralized agent-to-agent directory.

Model Context Protocol Server

Un repositorio de estudio para cuando probé un tutorial sobre cómo construir mi propio servidor MCP.

my-mcp-server

Un prototipo que utiliza mcp-framework para construir herramientas para IAs.

X MCP Server

Enables interaction with X (Twitter) API v2 with multi-user OAuth 2.0 support, allowing users to manage bookmarks, create tweets, and access user information with encrypted token storage and automatic refresh.

Task Scheduler MCP Server

Enables asynchronous task execution and scheduling using RabbitMQ, Celery, and Redis/SQLite. Supports immediate background task execution, scheduled tasks with ISO 8601 timestamps, task status monitoring, cancellation, and completion notifications.

mcp-semgrep-scanner

Run Semgrep static analysis from an AI agent. OWASP top 10, secrets detection, custom rule packs, baseline scanning. Curated by Archimedes Market with a verified Trust Report.

IMCP - Insecure Model Context Protocol

IMCP - Insecure Model Context Protocol The DVWA for AI Security! Welcome to IMCP – a deliberately vulnerable framework that exposes 16 critical security weaknesses in AI/ML systems. Whether you're a security researcher, developer, or educator, IMCP is your playground for hands-on learning about real

Ollama MCP Server

A Model Context Protocol (MCP) server that provides web search, web fetch, and chat completion capabilities using Ollama's Qwen3-coder models.

Luma API MCP

Enables image and video generation using Luma's API, supporting multiple models, aspect ratios, and advanced features like keyframes and style references.

receiptconverter-mcp

MCP server for ReceiptConverter that allows AI assistants to parse any receipt or invoice image/PDF into structured JSON with a single tool call.