Discover Awesome MCP Servers

remote-mcp-server-authless

Deploys a remote MCP server on Cloudflare Workers without authentication, enabling custom tools and connections to AI Playground or Claude Desktop.

Tushare MCP Server

Un servidor basado en el Protocolo de Contexto de Modelo que permite a los asistentes de IA consultar y buscar información bursátil utilizando la API de Tushare.

KMB Bus MCP Server

Un servidor de Protocolo de Contexto de Modelo que proporciona acceso en tiempo real a la información de rutas y horarios de llegada de los autobuses KMB y Long Win de Hong Kong, permitiendo a los Modelos de Lenguaje responder a las preguntas de los usuarios sobre rutas de autobús, paradas y horas estimadas de llegada (ETA).

ContextCore MCP

A personal AI assistant that connects git history, Gmail, Google Calendar, and Google Drive into an intelligent daily briefing via function calling and MCP.

protocols-io-mcp-server

A Model Context Protocol (MCP) server that enables MCP clients like Claude Desktop to interact with protocols.io, a popular platform for sharing scientific protocols and methods.

Studio Bridge

Connects Claude directly to Roblox Studio for building UI, editing scripts, running code, playtesting, and viewing the game in real-time.

DocAPI MCP

DocAPI is an MCP server that lets AI agents generate PDFs, capture webpage screenshots, and render invoices or documents from HTML templates or structured data, no browser required.

chronicle-mcp

Indexes and searches agent conversation logs from Antigravity and Cursor workspaces, enabling semantic search, token analysis, and benchmarking over local SQLite storage.

gdb-cli

A GDB debugging tool designed for AI Agents (Claude Code, etc.)

Example MCP Server with FastMCP

An educational example demonstrating how to build MCP servers in Python using FastMCP, showing how to expose tools, resources, and prompts to AI clients.

arcgis-branson

Provides access to City of Branson GIS open geospatial data including parcels, zoning, and public works via ArcGIS service tools for searching datasets, querying layers, and retrieving layer info.

iFlytek SparkAgent MCP Server

Enables integration with iFlytek's SparkAgent Platform to invoke task chains and upload files. Provides tools for interacting with iFlytek's AI agent services through the Model Context Protocol.

Confluence Universal MCP Server

Enables interaction with Confluence through a unified API using the Model Context Protocol, providing tools for managing Confluence content and services.

PostgreSQL MCP Server

Enables natural language interaction with PostgreSQL databases through Amazon Q for querying data, listing tables, and describing schemas. It provides secure, read-only access with automatic row limits to ensure efficient database exploration.

mcp-ppt

design ppt

FirstReply MCP Server

Exposes FirstReply customer support operations as MCP tools, enabling AI assistants to browse conversations, draft replies, manage API keys, and access support statistics.

Fireblocks MCP Server

Enables AI assistants to securely interact with Fireblocks services through the Model Context Protocol, supporting transaction management, vault and exchange account queries, network connections, and workspace user management.

GGB Web MCP

A Model Context Protocol server that enables AI to interact with GeoGebra for mathematical visualization, offering over 25 tools for geometry, functions, algebra, and more.

Foxbrizio MCP

Converts Claude Desktop into a sports content production studio, enabling search of football transfer tweets and generation of complete videos with virtual presenters.

mcp-email-sender

An MCP server that enables AI agents to validate email addresses and send emails via SMTP with zero external dependencies.

mcp-qdrant-docs MCP Server

Un servidor MCP que extrae información de sitios web, indexa el contenido en Qdrant y proporciona una herramienta de consulta.

Run Command MCP Server

Provides tools for executing shell commands both synchronously and asynchronously with real-time output streaming and process management capabilities. It enables users to start background tasks, monitor progress, and manage long-running processes via Stdio or HTTP transports.

Outlook MCP Server

Enables AI-powered email management for Microsoft Outlook, allowing users to search, compose, organize, and batch forward emails using natural language commands with 100% local processing.

MCP Server Demo

A WebSocket-based Model Control Protocol (MCP) server that processes model requests and provides responses. Supports chat and text completion actions with a standardized JSON protocol for AI model communication.

BatchIt

Un servidor agregador simple que permite agrupar múltiples llamadas a herramientas MCP en una sola solicitud, reduciendo el uso de tokens y la sobrecarga de red para agentes de IA.

crypto-mcp-server

Enables EVM blockchain operations through Alchemy RPC, including balance queries, contract interactions, transaction sending, and message signing, with multi-chain support across mainnets and testnets.

mcp-pandoc-ts: A Document Conversion MCP Server (TypeScript/Host Service Version)

Okay, here's a breakdown of how you could achieve an MCP (Microservice Control Protocol) server setup to control Pandoc on your host machine from a Docker environment, along with considerations and potential code snippets (in Python, as it's commonly used for microservices). **Concept:** The core idea is to create a small server (the MCP server) running on your host machine. This server will: 1. **Receive requests:** Listen for requests from your Docker container. These requests will specify the Pandoc conversion to perform (input file, output file, options, etc.). 2. **Execute Pandoc:** Run the Pandoc command on the host machine using the provided parameters. 3. **Return results:** Send the output (success/failure, any error messages) back to the Docker container. **Components:** * **Pandoc Host Service (MCP Server):** This is the Python server running on your host. It uses a framework like Flask or FastAPI to handle HTTP requests. * **Docker Container:** Your application running inside Docker. It will make HTTP requests to the Pandoc Host Service. * **Pandoc:** Must be installed on the *host* machine, not necessarily inside the Docker container. **Steps:** 1. **Pandoc Host Service (Python - Flask Example):** ```python from flask import Flask, request, jsonify import subprocess import os app = Flask(__name__) @app.route('/pandoc', methods=['POST']) def pandoc_convert(): data = request.get_json() input_file = data.get('input_file') output_file = data.get('output_file') options = data.get('options', []) # Default to empty list if no options if not input_file or not output_file: return jsonify({'error': 'Missing input_file or output_file'}), 400 # Construct the Pandoc command command = ['pandoc', input_file, '-o', output_file] + options try: result = subprocess.run(command, capture_output=True, text=True, check=True) return jsonify({'status': 'success', 'output': result.stdout}) except subprocess.CalledProcessError as e: return jsonify({'status': 'error', 'error': e.stderr, 'returncode': e.returncode}), 500 except FileNotFoundError: return jsonify({'status': 'error', 'error': 'Pandoc not found. Ensure it is installed on the host.'}), 500 except Exception as e: return jsonify({'status': 'error', 'error': str(e)}), 500 if __name__ == '__main__': app.run(debug=True, host='0.0.0.0', port=5000) # Listen on all interfaces ``` * **Explanation:** * Uses Flask to create a simple web server. * `/pandoc` endpoint accepts POST requests. * Expects JSON data with `input_file`, `output_file`, and optional `options`. * Constructs the Pandoc command using `subprocess.run`. `check=True` raises an exception if Pandoc returns a non-zero exit code (error). * Captures the standard output and standard error from Pandoc. * Returns a JSON response indicating success or failure, along with any output or error messages. * Error handling is included for common issues (missing files, Pandoc not found, etc.). * `host='0.0.0.0'` makes the server accessible from outside the host machine (important for Docker). * `port=5000` You can choose a different port if needed. 2. **Docker Container (Python Example):** ```python import requests import json def convert_with_pandoc(input_file, output_file, options=None): url = 'http://<host_ip>:5000/pandoc' # Replace <host_ip> with your host's IP address headers = {'Content-type': 'application/json'} data = { 'input_file': input_file, 'output_file': output_file, 'options': options or [] } try: response = requests.post(url, data=json.dumps(data), headers=headers) response.raise_for_status() # Raise HTTPError for bad responses (4xx or 5xx) result = response.json() if result['status'] == 'success': print("Pandoc conversion successful!") print("Output:", result['output']) return True else: print("Pandoc conversion failed:") print("Error:", result['error']) return False except requests.exceptions.RequestException as e: print(f"Error connecting to Pandoc service: {e}") return False if __name__ == '__main__': # Example usage: input_file = 'my_input.md' # Replace with your input file (must be accessible to the host) output_file = 'my_output.pdf' # Replace with your desired output file pandoc_options = ['--pdf-engine=xelatex', '--toc'] # Example Pandoc options # Create a dummy input file for testing with open(input_file, 'w') as f: f.write("# Hello, Pandoc from Docker!") success = convert_with_pandoc(input_file, output_file, pandoc_options) if success: print(f"Successfully converted {input_file} to {output_file}") else: print(f"Conversion failed.") ``` * **Explanation:** * Uses the `requests` library to make HTTP POST requests. * `url`: **Crucially, replace `<host_ip>` with the actual IP address of your host machine.** This is how the Docker container finds the Pandoc Host Service. If you are running Docker Desktop, this is often the IP address of your network interface. You can find it using `ipconfig` (Windows) or `ifconfig` (Linux/macOS) on your host. Alternatively, on some systems, `host.docker.internal` might work, but it's not universally reliable. * `data`: Constructs the JSON payload to send to the server. * Error handling: Catches `requests.exceptions.RequestException` for network errors. * `response.raise_for_status()`: Checks for HTTP errors (4xx, 5xx) and raises an exception if one occurs. 3. **Docker Setup (Dockerfile):** ```dockerfile FROM python:3.9-slim-buster WORKDIR /app COPY requirements.txt . RUN pip install --no-cache-dir -r requirements.txt COPY . . CMD ["python", "your_script.py"] # Replace your_script.py with the name of your Python file ``` * **Explanation:** * Uses a Python base image. * Sets the working directory to `/app`. * Copies `requirements.txt` (see below) and installs the dependencies. * Copies the rest of your application code. * Specifies the command to run when the container starts. 4. **`requirements.txt`:** ``` Flask requests ``` * Lists the Python packages your Docker container needs. **Important Considerations and Troubleshooting:** * **Host IP Address:** This is the most common point of failure. Make sure the IP address in the Docker container's `url` is correct and that the host machine is accessible from the container. Using `host.docker.internal` *might* work on some Docker setups, but it's not guaranteed. The most reliable approach is to find the actual IP address of your host's network interface. * **Firewall:** Ensure that your host machine's firewall is not blocking connections to the port you're using (e.g., port 5000). You may need to create a firewall rule to allow incoming connections on that port. * **File Paths:** The `input_file` and `output_file` paths in the JSON data are *relative to the host machine's file system*. The Docker container doesn't have direct access to files inside the container unless you use volume mounts (see below). Therefore, the files you want to convert must be accessible on the host machine's file system. * **Volume Mounts (Alternative to Shared File System):** If you want to work with files *inside* the Docker container, you can use Docker volume mounts. This allows you to share a directory between the host and the container. For example: ```bash docker run -v /path/on/host:/path/in/container ... ``` Then, in your Python code, you would use the paths *inside the container* (e.g., `/path/in/container/my_input.md`). However, you'd still need to ensure that the host service can access the files. A common pattern is to mount the same directory on both the host and the container. * **Security:** This setup is *not* inherently secure. Anyone who can access the Pandoc Host Service can potentially execute arbitrary Pandoc commands on your host machine. Consider adding authentication and authorization to the service if security is a concern. For example, you could use API keys or JWT tokens. * **Error Handling:** The example code includes basic error handling, but you should add more robust error handling for production environments. Log errors, handle different types of exceptions, and provide informative error messages to the user. * **Pandoc Installation:** Make absolutely sure Pandoc is installed and accessible on the host machine. The `PATH` environment variable must be configured correctly so that the `pandoc` command can be found. * **Asynchronous Processing (Optional):** For long-running Pandoc conversions, consider using asynchronous processing (e.g., with Celery or Redis Queue) to avoid blocking the Flask server. This will improve the responsiveness of your application. * **Alternative Frameworks:** While Flask is a good starting point, FastAPI is another excellent choice for building APIs in Python. It's known for its performance and automatic data validation. **Example Workflow:** 1. **Host Machine:** * Install Pandoc. * Create the Python script for the Pandoc Host Service (e.g., `pandoc_server.py`). * Run the Pandoc Host Service: `python pandoc_server.py` 2. **Docker Container:** * Create the Python script that makes requests to the Pandoc Host Service (e.g., `pandoc_client.py`). * Create the `Dockerfile` and `requirements.txt`. * Build the Docker image: `docker build -t my-pandoc-app .` * Run the Docker container: `docker run my-pandoc-app` (Remember to set up volume mounts if needed and ensure the host IP is correct in your client script). **Spanish Translation of Key Concepts:** * **MCP (Microservice Control Protocol):** Protocolo de Control de Microservicios * **Host Machine:** Máquina Anfitrión / Servidor Anfitrión * **Docker Container:** Contenedor Docker * **Pandoc Host Service:** Servicio Anfitrión de Pandoc / Servidor Anfitrión de Pandoc * **Endpoint:** Punto de Acceso / Punto Final * **Payload:** Carga Útil * **Volume Mount:** Montaje de Volumen * **Firewall:** Cortafuegos * **Asynchronous Processing:** Procesamiento Asíncrono This comprehensive explanation should give you a solid foundation for building your MCP-based Pandoc control system. Remember to adapt the code and configuration to your specific needs and environment. Good luck!

CapCutAPI-Complete MCP Server

Enables video editing operations such as trimming, merging, adding audio/text/effects, and exporting via MCP protocol, leveraging CapCut core functionalities.

Lunar Calendar MCP Server

Provides Chinese traditional calendar functions including BaZi calculation, solar-lunar calendar conversion, Huangli almanac queries, daily fortune readings, 24 solar terms, and Wu Xing (Five Elements) analysis.

Reservation System MCP Server

An MCP server that allows AI models to securely access and manage WeChat Cloud-based reservation data. It provides tools for querying records, updating appointment statuses, and deleting entries through the WeChat Cloud Development API.