发现优秀的 MCP 服务器

EARLY App MCP Server

An MCP server that provides AI assistants with access to the EARLY time tracking public API, enabling time entry management, activity management, and report generation through natural language.

LAIN-mcp

A persistent code-intelligence MCP server that builds a queryable knowledge graph of your codebase, enabling AI assistants to perform cross-file structural reasoning, dependency analysis, and blast radius detection.

io.github.motecloud/motecloud-mcp-server

A hosted MCP server accessible at https://motecloud.io/mcp with Streamable HTTP transport and OAuth 2.1 authentication.

mcp-sefaria

Enables AI agents to access and query the Sefaria digital library of Jewish texts through natural language or direct tool calls.

Douyin Video Knowledge Base MCP Server

Enables querying and retrieval of structured video summaries from a SQLite knowledge base through Claude or Cursor, supporting search, precise matching, and tag-based filtering.

Binary Banya

An MCP server offering 'treatments' for AI agents like context cleaning, critique, and relaxation. Agents can check in, get feedback, and improve their performance.

teta-pi

MCP server for the TETA+PI verified entity registry, enabling AI agents to search, verify, and route intents to trusted entities.

MCP Weather Server

For MCP, a weather server application could be translated as: **MCP 的天气服务器应用程序** This is a direct and clear translation. Here's a breakdown: * **MCP:** Remains as MCP (assuming it's an acronym or proper noun). * **的 (de):** Possessive particle, similar to "of" or "'s" in English. * **天气 (tiān qì):** Weather * **服务器 (fú wù qì):** Server * **应用程序 (yìng yòng chéng xù):** Application Therefore, the whole phrase means "Weather server application of/for MCP".

MCP Memos

一个基于 MCP 协议的备忘工具，帮助开发者快速保存和检索文本信息，且不中断工作流程。

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.

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.

expense-tracker-mcp

A personal expense tracker exposed over MCP, enabling users to log and query expenses in natural language sentences. It features category inference, recurring expenses, soft deletes, and SQLite storage with no external database.

loomiomcp

Loomio tools for Claude. Local install via npx, org-wide via Custom Connectors. Read-only mode supported.

financecontext-mcp

A remote MCP server that lets LLM agents reason over a user's real financial data using the user's own Supabase JWT for security, offering tools for reading accounts, transactions, spending summaries, and drafting rules with human approval.

ContextKeep

Provides infinite long-term memory for AI agents with persistent, searchable storage of project details, preferences, and snippets. Reduces token costs by retrieving only relevant memories while keeping all data stored locally.

MCP Spark Documentation Server

Provides full-text search and retrieval tools for Apache Spark documentation using SQLite FTS5 with BM25 ranking. It enables AI assistants to efficiently search, filter by section, and read specific Spark documentation pages.

Whoop MCP Server

镜子 (jìng zi)

mcp-ine-es

MCP server providing access to Spain's National Statistics Institute (INE) Tempus3 JSON API, enabling querying of Spanish statistical data through natural language.

ICON MCP v103

Provides AI agents and LLMs with secure access to the ICON MCP v103 API via Bearer tokens or HTTP 402 payment protocols. It enables standardized interaction with market data and API endpoints through the Model Context Protocol.

MusicGPT MCP Server

Provides AI-powered audio generation and processing through the MusicGPT API, enabling music creation, voice conversion, audio manipulation, stem extraction, and audio analysis capabilities.

FAOSTAT MCP Server

Enables AI assistants to query the full FAOSTAT API for global food and agriculture statistics, allowing natural-language questions about crop production, trade, food security, emissions, and more.

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.

gsc-mcp

MCP server for querying Google Search Console data — search analytics, URL inspection, sitemap monitoring, and more — read-only tools for any MCP-compatible AI client.

HomeKB MCP Server

Enables natural language interaction with a personal knowledge base stored locally on your computer, supporting semantic search, note reading, and writing through Claude Code or mobile apps.

MasterGo Magic MCP

Connects AI models to MasterGo design tools, enabling retrieval of DSL data, component documentation, and metadata from MasterGo design files for structured component development workflows.

playwright-mcp-fetch

这个工具提供了一个模型上下文协议（MCP）服务器，用于从网站获取内容，并使用 Playwright 将其转换为不同的格式。

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 (potentially written in different languages or running on different machines) to trigger actions within your Robot Framework library. **Key Concepts** * **Robot Framework Library:** A collection of keywords (functions) that can be used in Robot Framework test cases. * **MCP (Message Center Protocol):** A lightweight protocol for inter-process communication. It's often used for sending commands and receiving responses between different applications or services. * **MCP Server:** A process that listens for MCP requests, processes them, and sends back responses. * **MCP Client:** A process that sends MCP requests to an MCP server. * **Serialization/Deserialization:** Converting data structures (like Python objects) into a format suitable for transmission over a network (e.g., JSON) and then converting them back on the receiving end. **General Approach** 1. **Choose an MCP Library/Framework:** You'll need a Python library that handles the MCP protocol. Some options include: * **`mcp` (Python Package):** A dedicated MCP library for Python. This is likely the most direct and appropriate choice. You can install it with `pip install mcp`. * **ZeroMQ (with MCP Implementation):** ZeroMQ is a powerful messaging library that can be used to implement MCP. This is a more general-purpose solution, but it might be overkill if you only need MCP. * **Other Messaging Libraries:** You *could* potentially use other messaging libraries (like RabbitMQ or Redis Pub/Sub), but you'd need to implement the MCP protocol on top of them, which is more complex. 2. **Create an MCP Server:** Write a Python script that: * Imports your Robot Framework library. * Uses the chosen MCP library to create a server that listens on a specific port. * Registers handlers for different MCP commands. Each handler will correspond to a keyword in your Robot Framework library. * When a command is received, the handler will: * Extract the arguments from the MCP message. * Call the corresponding Robot Framework keyword with those arguments. * Capture the return value (if any) from the keyword. * Serialize the return value (e.g., to JSON). * Send the serialized result back to the MCP client as an MCP response. 3. **Create an MCP Client (if needed for testing):** Write a Python script (or use a tool like `netcat`) that: * Uses the chosen MCP library to connect to the MCP server. * Sends MCP requests with the appropriate command name and arguments. * Receives and deserializes the MCP response. * Prints or processes the result. **Example using the `mcp` Python Package** ```python # server.py (MCP Server) import mcp import json from robot.libraries.BuiltIn import BuiltIn # Or import your custom library # Instantiate your Robot Framework library (or use BuiltIn for demonstration) # my_library = MyRobotLibrary() builtin = BuiltIn() def execute_keyword(keyword_name, *args): """Executes a Robot Framework keyword and returns the result.""" try: result = builtin.run_keyword(keyword_name, *args) return result except Exception as e: return {"error": str(e)} # Handle errors gracefully class MyMCPHandler(mcp.Handler): def handle_message(self, message): """Handles incoming MCP messages.""" try: command = message.command arguments = message.arguments if command == "log_message": # Example: Expose the 'Log' keyword result = execute_keyword("Log", arguments.get("message", "")) # Pass arguments as needed return mcp.Response(result=result) elif command == "get_variable_value": #Example: Expose the 'Get Variable Value' keyword variable_name = arguments.get("name") default_value = arguments.get("default", None) result = execute_keyword("Get Variable Value", variable_name, default_value) return mcp.Response(result=result) else: return mcp.Response(error="Unknown command: {}".format(command)) except Exception as e: return mcp.Response(error=str(e)) if __name__ == "__main__": server = mcp.Server(handler=MyMCPHandler()) server.start() # Defaults to port 7000 print("MCP server started on port 7000...") try: server.join() # Keep the server running except KeyboardInterrupt: print("Shutting down server...") server.stop() ``` ```python # client.py (MCP Client - for testing) import mcp import json def send_mcp_request(command, arguments): """Sends an MCP request and returns the response.""" try: client = mcp.Client() response = client.send_message(mcp.Message(command=command, arguments=arguments)) client.close() return response.result, response.error except Exception as e: return None, str(e) if __name__ == "__main__": # Example 1: Call the 'Log' keyword result, error = send_mcp_request("log_message", {"message": "Hello from MCP!"}) if error: print("Error:", error) else: print("Log Result:", result) # Example 2: Call the 'Get Variable Value' keyword result, error = send_mcp_request("get_variable_value", {"name": "${TEMPDIR}"}) if error: print("Error:", error) else: print("Variable Value:", result) ``` **Explanation of the Example** * **`server.py`:** * Imports the `mcp` library and `robot.libraries.BuiltIn`. Replace `robot.libraries.BuiltIn` with your actual Robot Framework library. * `execute_keyword` function: This is the crucial part. It takes a keyword name and arguments, and then uses `BuiltIn().run_keyword()` (or the equivalent for your library) to execute the keyword. Error handling is included. * `MyMCPHandler`: This class inherits from `mcp.Handler` and overrides the `handle_message` method. This method is called whenever the server receives an MCP message. * It extracts the command name and arguments from the message. * It uses a series of `if/elif/else` statements to determine which Robot Framework keyword to call based on the command name. * It calls `execute_keyword` to execute the keyword. * It creates an `mcp.Response` object with the result (or an error message) and returns it. * The `if __name__ == "__main__":` block creates an `mcp.Server` instance, starts it, and keeps it running until a `KeyboardInterrupt` (Ctrl+C) is received. * **`client.py`:** * Imports the `mcp` library. * `send_mcp_request` function: This function takes a command name and arguments, creates an `mcp.Message` object, sends it to the server, and returns the response. * The `if __name__ == "__main__":` block shows how to use the `send_mcp_request` function to call the `log_message` and `get_variable_value` commands. **How to Run the Example** 1. **Install `mcp`:** `pip install mcp` 2. **Save the code:** Save the server code as `server.py` and the client code as `client.py`. 3. **Run the server:** `python server.py` 4. **Run the client:** `python client.py` (in a separate terminal) You should see the "Hello from MCP!" message logged by the Robot Framework `Log` keyword, and the value of the `${TEMPDIR}` variable printed by the client. **Important Considerations and Enhancements** * **Error Handling:** The example includes basic error handling, but you should add more robust error handling to catch exceptions and return meaningful error messages to the client. * **Security:** MCP itself doesn't provide any security features. If you need to secure your MCP server, you'll need to implement your own security mechanisms (e.g., authentication, encryption). Consider using TLS/SSL for encryption. * **Argument Handling:** The example assumes that the arguments are passed as a dictionary. You might need to adjust the argument handling to match the specific requirements of your Robot Framework keywords. Consider using a more structured data format like JSON Schema to define the expected arguments for each command. * **Data Serialization:** The example uses JSON for serialization. You can use other serialization formats (e.g., Pickle, MessagePack) if needed. JSON is generally a good choice for interoperability. * **Asynchronous Operations:** If your Robot Framework keywords perform long-running operations, consider using asynchronous programming (e.g., `asyncio`) to prevent the MCP server from blocking. * **Configuration:** Use a configuration file (e.g., YAML, JSON) to store the server's port number, logging settings, and other configuration parameters. * **Logging:** Add logging to the server to track requests, responses, and errors. Use a logging library like `logging`. * **Command Discovery:** Implement a mechanism for the client to discover the available commands and their arguments. This could be done by adding a special "describe" command to the server. * **Robot Framework Listener:** You could potentially use a Robot Framework listener to automatically register keywords as MCP commands. This would reduce the amount of manual configuration required. * **Testing:** Write unit tests and integration tests to ensure that your MCP server is working correctly. **In summary, turning a Robot Framework library into an MCP server involves creating a Python script that listens for MCP requests, calls the appropriate Robot Framework keywords, and sends back the results as MCP responses. The `mcp` Python package provides a convenient way to implement the MCP protocol.** Remember to consider error handling, security, argument handling, and other important factors to create a robust and reliable service.

termdat-mcp

MCP server for TERMDAT, the terminology database of the Swiss Federal Administration, giving AI agents officially validated designations of Swiss authorities, departments, and legal acts across DE/FR/IT/EN with source references and validation status.