Discover Awesome MCP Servers

DaVinci MCP Professional

A professional implementation of a Model Context Protocol server that enables integration between DaVinci Resolve video editing software and AI assistants like Claude Desktop or Cursor.

MCP Dynamic Tools

A dynamic MCP server that automatically discovers Python files in a directory and exposes them as tools to any MCP-compatible AI client, allowing users to easily create and deploy custom AI tools.

Maven Version Server

An MCP server for managing Maven dependency versions using direct metadata parsing from Maven Central. It provides tools to fetch latest stable versions, list version history, and compare versions with upgrade recommendations.

WeChat MCP Server

Enables AI assistants to send WeChat messages through automation, supporting both immediate and scheduled message delivery to contacts and groups using the Model Context Protocol standard.

EmailAssistant

An MCP-compatible server that integrates with Gmail API to fetch and summarize emails based on custom queries or recent activity.

PureScript MCP Server

A Model Context Protocol server that provides PureScript development tools for AI assistants, enabling code analysis, type lookups, and IDE server management without requiring heavy IDE setup.

DateTime-LocalMCPServer

A Model Context Protocol server built with FastMCP that provides tools for retrieving the current local date and time. It allows LLMs to access precise temporal information directly from the host environment.

MCP TypeScript Demo Server

A TypeScript implementation of the Model Context Protocol server that enables searching arXiv papers and extracting paper information through standardized client-server communication.

Weather MCP Server

Provides real-time weather data and current conditions for major Chinese cities and global locations using the wttr.in API. This HelloAgents-based server enables querying of temperature, humidity, and weather status without requiring an external API key.

mcp-dev-record

A dedicated MCP (Model Context Protocol) server for recording and organizing conversation content, providing templated recording functionality.

Custom MCP Servers

Servidores MCP personalizados

Feishu Access Token MCP

Manages and automatically refreshes Feishu (Lark) app access tokens and user access tokens, enabling secure authentication with Feishu APIs through session-based configuration.

CallHub

CallHub MCP is a Python-based tool that allows you to interact with the CallHub API through Claude. This tool provides a comprehensive set of functions for managing contacts, phonebooks, agents, teams, campaigns, and other CallHub resources.

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.

Tekuila MCP Server

An MCP server that provides access to Tekuila restaurant menus with AI-powered health recommendations and meal planning. It enables users to retrieve daily or weekly menus and receive intelligent nutritional analysis through clients like Claude Desktop and Cursor.

Sequa MCP

Enables AI assistants to access contextual knowledge from multiple repositories through Sequa's Contextual Knowledge Engine. Provides architecture-aware code understanding and cross-repository context for more accurate, production-ready code generation.

Host Terminal MCP

Enables AI assistants to execute terminal commands on a host machine with configurable, granular permission controls and safety protections. It features multiple security modes, including allowlists and manual approval, to ensure safe command execution within specified directories.

MCP Server Legifrance

A server implementing the Model Context Protocol to allow direct access to French legal resources (laws, codes, case law) from compatible Large Language Models like Claude, enabling interactive legal research through the Legifrance API.

QuickSight MCP Server

An auto-generated MCP server that enables interaction with AWS QuickSight services through the Model Context Protocol. Provides programmatic access to QuickSight's business intelligence and analytics capabilities via the AWS QuickSight OpenAPI specification.

Begagnad MCP

Enables AI agents to search and retrieve listings from Sweden's largest second-hand marketplaces, Blocket and Tradera. Returns unified data including prices, images, seller information, and direct links to listings.

mcp-gitlab

mcp-gitlab

Basecamp MCP Server

Enables seamless integration with Basecamp 3 through 46 comprehensive API tools, allowing users to manage projects, todos, card tables, documents, campfire messages, and other Basecamp features through natural language interactions in Claude Desktop and Cursor IDE.

Weather MCP Server

Provides weather information through the Model Context Protocol, fetching real-time weather data from OpenWeather API or using simulated data when API key is not configured.

DISCLAIMER

Servidor MCP para la API OAI-PMH del Rijksmuseum

MCP Exploit-DB Server

Provides access to the Exploit-DB database through the searchsploit tool, enabling AI assistants to search for exploits by keywords or CVE identifiers, retrieve exploit content, and manage the local exploit database.

SwaggerMcp

A service that dynamically generates MCP tools from Swagger/OpenAPI documentation, allowing Claude Desktop to directly invoke REST APIs through natural language.

Context Persistence MCP Server

Enables cross-window context sharing in VS Code by persisting Copilot conversations to SQLite, allowing code discussions from one repository to be accessible in other VS Code windows through smart entity matching and search.

MCP AWS SES Email Server

Enables sending emails through AWS Simple Email Service with support for single emails, bulk personalized emails, and template management. Deployed on Cloudflare Workers for fast, global edge performance with full TypeScript type safety.