Discover Awesome MCP Servers

macOS Office 365 MCP Server

Enables AI assistants to create and manipulate Microsoft Office documents (PowerPoint, Word, Excel) on macOS via AppleScript automation.

System Diagnostics MCP Server

Provides system diagnostic capabilities including CPU, memory, disk usage, top processes, and port status checking.

MIMO Image Recognition MCP

A Node.js MCP server that enables image understanding by calling the MIMO multimodal model, supporting local file paths and URLs.

Roblox MCP Unified Server

Comprehensive Roblox development server with 8 tools for managing Lua/Luau scripts including creation, validation, backup/restore, and project management with SQLite storage and HMAC authentication.

selenium-recorder-mcp

Records browser interactions (clicks, DOM mutations, console logs, JS errors) via Chrome DevTools Protocol while you manually navigate, and provides tools to retrieve and analyze recordings for test generation.

WeatherAPI MCP Server

Menyediakan data cuaca dan kualitas udara terkini untuk kota manapun menggunakan WeatherAPI, terintegrasi dengan mudah dengan klien MCP seperti n8n dan Aplikasi Desktop Claude.

CodeMerge

A Model Context Protocol server that uses Osmosis-Apply-1.7B to intelligently apply code edits while preserving the structure of the original code.

rendex-mcp

Capture screenshots, generate PDFs, and render HTML to images via AI agents. Supports batch capture, geo-targeting, async webhooks, and CSS/JS injection.

Acunetix MCP Server

Exposes the Acunetix Scanner API as a streamable HTTP MCP server, allowing clients to interact with security scanning tools via standardized requests. It enables automated vulnerability management and integration with platforms like GhostReconRev through a containerized bridge.

opendocswork-mcp

Rust-native MCP server for Office document processing (Excel, Word, PowerPoint) enabling sub-millisecond, local-first document manipulation and export to PDF.

Whoop MCP Server

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TShark2MCP

An MCP server that enables AI-assisted network packet analysis using Wireshark's TShark tool. It provides tools for pcap file overview, session extraction, protocol filtering, and statistical analysis through a standardized interface.

rekordbox-mcp

MCP server for rekordbox DJ database access. Provides read-only querying of tracks, playlists, and DJ session history from encrypted rekordbox SQLite databases using pyrekordbox.

Cloudflare Remote MCP Server (Authless)

Enables deployment of a remote MCP server on Cloudflare Workers without authentication, supporting custom tools and integration with clients like Claude Desktop.

RateSpot MCP Server

Provides access to RateSpot.io mortgage rate APIs, enabling AI assistants to fetch real-time mortgage rates, compare loan products, calculate payments, and access comprehensive lending information.

Photonics Simulation MCP

Knowledge-based MCP server for photonics simulation engineering, providing rules, bugs, checklists, and tool recommendations for Lumerical FDTD/MODE/FDE, HFSS, COMSOL, and PyAEDT.

mcp-book-search

Server MCP untuk pencarian buku domestik.

Renderer MCP Server

AI-powered assistant for the Renderer portfolio framework. Enables users to explore documentation, validate TOML configurations, generate templates, and customize portfolios through natural language.

RobotFrameworkLibrary-to-MCP

Okay, here's a breakdown of how to turn a Robot Framework library into an MCP (Message Center Protocol) server, along with explanations and considerations: **Understanding the Goal** The core idea is to expose the functionality of your Robot Framework library as a service that can be accessed remotely via MCP. This allows other systems (clients) to call the keywords in your library without needing to run Robot Framework directly on the client machine. **Key Components and Concepts** 1. **Robot Framework Library:** This is your existing library containing the keywords you want to expose. 2. **MCP Server:** A server that listens for MCP requests, processes them, and sends back responses. You'll need to implement this server. 3. **MCP Client:** The system that sends requests to your MCP server to execute keywords. 4. **Serialization/Deserialization:** MCP involves sending data (keyword names, arguments, return values) over a network. You'll need to serialize data into a format suitable for transmission (e.g., JSON, XML, Protocol Buffers) and deserialize it on the other end. **General Steps** 1. **Choose an MCP Implementation (or Build Your Own):** * **Existing MCP Libraries (Python):** Check if there are existing Python libraries that provide MCP server/client functionality. Search for "Python MCP library" or "Message Center Protocol Python." If you find a suitable library, it will greatly simplify the process. I don't have specific recommendations without knowing your exact requirements, but this is the first place to look. * **Roll Your Own (using sockets):** If you can't find a suitable library, you'll need to implement the MCP protocol yourself using Python's socket library. This is more complex but gives you full control. You'll need to understand the MCP specification. 2. **Create the MCP Server (Python):** * **Import Your Robot Framework Library:** In your Python MCP server code, import the Robot Framework library you want to expose. * **Listen for Connections:** Use the chosen MCP library (or socket code) to listen for incoming connections on a specific port. * **Receive MCP Requests:** When a client connects, receive the MCP request. The request will typically contain: * The name of the Robot Framework keyword to execute. * The arguments to pass to the keyword. * **Deserialize the Request:** Convert the received data (e.g., JSON string) into Python data structures (e.g., a dictionary or list). * **Execute the Keyword:** ```python # Assuming you have your library imported as 'mylibrary' def handle_mcp_request(keyword_name, args): try: # Use getattr to dynamically call the keyword keyword_function = getattr(mylibrary, keyword_name) result = keyword_function(*args) # Execute the keyword return result, None # Return result and no error except Exception as e: return None, str(e) # Return None and the error message ``` * **Serialize the Response:** Convert the result (or any error message) into a format suitable for sending back to the client (e.g., JSON). * **Send the Response:** Send the serialized response back to the client. * **Close the Connection:** Close the connection with the client. 3. **Create the MCP Client (Python or other language):** * **Connect to the Server:** Use the chosen MCP library (or socket code) to connect to the MCP server's address and port. * **Create the MCP Request:** Construct the MCP request, including the keyword name and arguments. * **Serialize the Request:** Convert the request data into the chosen format (e.g., JSON). * **Send the Request:** Send the serialized request to the server. * **Receive the Response:** Receive the response from the server. * **Deserialize the Response:** Convert the received data back into Python data structures. * **Process the Result:** Handle the result (or any error message) returned by the server. * **Close the Connection:** Close the connection with the server. **Example (Illustrative - Using Sockets and JSON for Simplicity)** This is a simplified example to illustrate the concepts. It's not a complete, production-ready solution. *Server (server.py)* ```python import socket import json import mylibrary # Your Robot Framework library HOST = '127.0.0.1' # Standard loopback interface address (localhost) PORT = 65432 # Port to listen on (non-privileged ports are > 1023) def handle_request(data): try: request = json.loads(data.decode('utf-8')) keyword_name = request['keyword'] args = request['args'] keyword_function = getattr(mylibrary, keyword_name) result = keyword_function(*args) return json.dumps({'result': result, 'error': None}) except Exception as e: return json.dumps({'result': None, 'error': str(e)}) with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: s.bind((HOST, PORT)) s.listen() print(f"Server listening on {HOST}:{PORT}") conn, addr = s.accept() with conn: print(f"Connected by {addr}") while True: data = conn.recv(1024) if not data: break response = handle_request(data) conn.sendall(response.encode('utf-8')) ``` *Client (client.py)* ```python import socket import json HOST = '127.0.0.1' # The server's hostname or IP address PORT = 65432 # The port used by the server with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: s.connect((HOST, PORT)) request = { 'keyword': 'my_keyword', # Replace with your keyword name 'args': ['arg1', 'arg2'] # Replace with your arguments } request_json = json.dumps(request) s.sendall(request_json.encode('utf-8')) data = s.recv(1024) print('Received:', repr(data)) ``` *Example Robot Framework Library (mylibrary.py)* ```python def my_keyword(arg1, arg2): """This is a sample keyword.""" return f"Keyword called with {arg1} and {arg2}" def another_keyword(number): return number * 2 ``` **Important Considerations** * **Error Handling:** Robust error handling is crucial. Catch exceptions in the server and send meaningful error messages back to the client. * **Security:** If you're exposing this service over a network, consider security implications. Use encryption (e.g., TLS/SSL) to protect the data in transit. Implement authentication and authorization to control who can access the service. * **Data Types:** Be mindful of data types when serializing and deserializing. Ensure that the client and server agree on how to represent data (e.g., dates, numbers). * **Concurrency:** If you expect multiple clients to connect simultaneously, you'll need to handle concurrency in your server (e.g., using threads or asynchronous programming). * **MCP Specification:** If you are implementing the MCP protocol yourself, carefully study the MCP specification to ensure compliance. * **Existing Libraries:** Before you start writing a lot of code, thoroughly research existing Python libraries that might provide MCP functionality. This can save you a significant amount of time and effort. **How to Run the Example** 1. **Save the files:** Save the code as `server.py`, `client.py`, and `mylibrary.py` in the same directory. 2. **Run the server:** Open a terminal and run `python server.py`. 3. **Run the client:** Open another terminal and run `python client.py`. The client will send a request to the server, the server will execute the `my_keyword` function from `mylibrary.py`, and the client will print the response. Remember to replace `"my_keyword"` and the arguments in `client.py` with the actual keyword and arguments from your Robot Framework library. Also, replace `mylibrary` in `server.py` with the actual name of your library. This detailed explanation and example should give you a solid foundation for turning your Robot Framework library into an MCP server. Good luck!

aster-guard

Enables scanning MCP server configurations for security risks like prompt injection, hardcoded secrets, and dangerous commands, providing risk scores and detailed reports before connecting to an AI coding assistant.

Toast MCP Integration

An MCP server that enables Claude Desktop to interact with Toast restaurant data, including sales summaries, top items, and product mix analysis.

DevsContext

DevsContext is an MCP server that provides AI coding agents with synthesized engineering context—requirements, decisions, architecture, and standards—from tools like Jira and Slack. It fetches and synthesizes relevant information on demand to help agents work on tasks correctly.

vmware-nsx

AI-powered VMware NSX networking management. Configure segments, gateways, NAT, routing, and IPAM via natural language with 31 MCP tools.

nexi-xpay-mcp-server

Enables AI assistants to query orders, transaction details, warnings/anomalies, and payment methods from your Nexi XPay merchant account.

resume-markdown-mcp

Write your resume in Markdown and export to PDF with AI assistance, using a built-in stylesheet and tools for conversion.

Zammad MCP Server

An MCP server that connects AI assistants to Zammad, providing tools for managing tickets, users, organizations, and attachments.

Cloudflare Remote MCP Server

A template for deploying remote Model Context Protocol servers on Cloudflare Workers using Server-Sent Events (SSE). It enables the creation and hosting of custom AI tools that can be accessed via the Cloudflare AI Playground or local clients like Claude Desktop.

scala-mcp-server

MCP server for S.C.A.L.A. Score API — search and retrieve data on 244M+ companies across 50+ countries. Tools for company lookup by name/VAT/ID, NACE sector search, geographic filtering, and financial data enrichment from official EU business registries.

TaskMateAI

Aplikasi manajemen tugas berbasis AI yang beroperasi melalui MCP, memungkinkan pembuatan, pengorganisasian, dan pelaksanaan tugas secara mandiri dengan dukungan untuk sub-tugas, prioritas, dan pelacakan kemajuan.