Discover Awesome MCP Servers

Google Docs MCP Server

Enables Claude to read, create, and update Google Docs through OAuth authentication. Deployed as a remote Next.js server on Vercel with support for multiple users and secure API key management.

MCP Agents Server

Facilitates interaction and context sharing between AI models using the standardized Model Context Protocol (MCP) with features like interoperability, scalability, security, and flexibility across diverse AI systems.

Time MCP Server

Provides current time information and timezone conversion capabilities using IANA timezone names. Enables LLMs to get current time in any timezone and convert times between different timezones with automatic system timezone detection.

Ping MCP Server

An MCP server that allows users to ping other hosts to check network connectivity and diagnostic information. It enables LLMs to perform network latency tests and host availability checks through a standardized interface.

Fathom MCP Server

Enables AI agents to access Fathom AI meeting data including transcripts, summaries, teams, and team members. Provides tools to list meetings with filters, retrieve detailed transcripts and summaries, and manage team information.

MCP Spec Generator

Generates project specifications and file structures using Claude AI through MCP integration. Enables users to describe project ideas and automatically create corresponding documentation and project files.

MCP Plus

A lightweight MCP server that enhances AI agents with tools for codebase analysis, task delegation to sub-agents, multi-agent coordination through chatrooms, and project todo management.

Cursor n8n Builder

Enables AI assistants in Cursor IDE to manage n8n workflows through the n8n REST API, including creating, updating, activating workflows, viewing execution history, and triggering webhooks.

CC Explorer MCP Server

Enables AI assistants to query the Canton Network blockchain explorer through the CC Explorer Pro API. It provides tools for accessing ledger updates, governance votes, validator information, and network consensus data.

Macroforge MCP Server

Enables AI assistants to access Macroforge documentation, validate TypeScript code with @derive decorators, expand macros, and retrieve macro documentation.

REMnux Documentation MCP Server

Enables AI assistants to search and retrieve information from the REMnux malware analysis toolkit documentation, allowing users to ask questions about reverse-engineering tools and get accurate, up-to-date answers from the live documentation.

Loop MCP Server

Enables LLMs to process arrays item-by-item or in batches with a specific task, storing and retrieving results with optional summarization after completion.

PostgreSQL MCP Server

Enables interaction with PostgreSQL databases through both HTTP and stdio transports. Supports executing read-only SQL queries, listing tables, and retrieving schema information with stateful session management.

mcp-academia-server

An MCP server specialized in managing and querying gym exercises through a centralized database. It enables users to search for workouts by name or muscle group and retrieve detailed information including sets, repetitions, and rest intervals.

TOPdesk MCP Server

Enables interaction with TOPdesk service management platform through comprehensive API integration. Supports incident management, operator/person operations, document conversion, and FIQL querying for streamlined IT service desk workflows.

my-design MCP Server

An MCP server that enables AI to interact with the private 'my-design' React component library and design tokens for UI generation and technical support. It provides tools for component searching, API documentation retrieval, and migration guidance based on specific internal design specifications.

MCP to LangChain/LangGraph Adapter

Okay, I understand. You want a Python adapter that takes tools designed for an MCP (presumably Minecraft Protocol) server and makes them usable as tools within the Langchain framework. Here's a conceptual outline and a code snippet to get you started. This is a basic example and will need to be adapted to your specific MCP server tools and Langchain setup. **Conceptual Outline** 1. **Understand MCP Server Tools:** You need to know how to interact with your MCP server tools. This likely involves sending commands over a network connection (e.g., TCP socket) and parsing the responses. You'll need to define the specific commands each tool accepts and the format of the data it returns. 2. **Langchain Tool Interface:** Langchain tools have a specific interface. They need a `name`, a `description`, and a `_run` method (or `arun` for asynchronous execution) that takes a string as input and returns a string as output. 3. **Adapter Class:** Create a Python class that inherits from `langchain.tools.BaseTool`. This class will: * Initialize with the necessary connection details for your MCP server. * Implement the `_run` method to: * Connect to the MCP server. * Format the input string into a command suitable for the MCP server tool. * Send the command to the server. * Receive the response from the server. * Parse the response. * Return the parsed response as a string. * Define the `name` and `description` attributes for the tool. **Code Snippet (Basic Example)** ```python from langchain.tools import BaseTool import socket import json # Assuming your MCP server uses JSON for communication class MCPServerTool(BaseTool): """Tool for interacting with an MCP server.""" name: str = "mcp_server_tool" # Replace with a more descriptive name description: str = ( "Useful for interacting with a Minecraft server. " "Input should be a JSON string containing the 'command' and any necessary 'arguments'." ) host: str = "localhost" # Replace with your MCP server's host port: int = 25565 # Replace with your MCP server's port def _run(self, query: str) -> str: """Use the tool.""" try: query_json = json.loads(query) command = query_json.get("command") arguments = query_json.get("arguments", {}) # Default to empty dict if no arguments if not command: return "Error: 'command' key missing in the input JSON." # Format the command for the MCP server (adjust as needed) mcp_command = self._format_mcp_command(command, arguments) # Connect to the MCP server with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: s.connect((self.host, self.port)) s.sendall(mcp_command.encode()) # Encode to bytes # Receive the response (adjust buffer size as needed) response = s.recv(1024).decode() # Decode from bytes # Parse the response (assuming JSON) try: response_json = json.loads(response) return json.dumps(response_json) # Return as JSON string except json.JSONDecodeError: return f"MCP Server Response: {response}" # Return raw response if not JSON except json.JSONDecodeError: return "Error: Invalid JSON input." except Exception as e: return f"Error: {e}" async def _arun(self, query: str) -> str: """Use the tool asynchronously.""" raise NotImplementedError("This tool does not support asynchronous execution.") def _format_mcp_command(self, command: str, arguments: dict) -> str: """Formats the command for the MCP server. Adjust this based on your server's protocol.""" # Example: Assuming the server expects commands like "command arg1=value1 arg2=value2" command_string = command for key, value in arguments.items(): command_string += f" {key}={value}" return command_string ``` **How to Use** 1. **Replace Placeholders:** Fill in the `host`, `port`, `name`, and `description` attributes with the correct values for your MCP server and tool. 2. **Implement `_format_mcp_command`:** This is crucial. This method *must* format the input `command` and `arguments` into the exact format that your MCP server expects. This will vary depending on your server's protocol. 3. **Adjust Socket Communication:** The `socket` code might need adjustments. Consider: * **Buffering:** The `recv(1024)` call receives up to 1024 bytes. If your server sends larger responses, you'll need to receive in a loop until you have the complete response. * **Encoding:** Make sure you're using the correct encoding (e.g., UTF-8) for both sending and receiving data. * **Error Handling:** Add more robust error handling (e.g., timeouts, connection errors). 4. **Parse Responses:** The code assumes the server returns JSON. If it returns a different format, you'll need to change the parsing logic accordingly. 5. **Add to Langchain:** Instantiate the `MCPServerTool` and add it to your Langchain agent's tools list. ```python # Example of adding the tool to a Langchain agent from langchain.agents import initialize_agent from langchain.llms import OpenAI # Or your preferred LLM # Replace with your OpenAI API key openai_api_key = "YOUR_OPENAI_API_KEY" llm = OpenAI(temperature=0, openai_api_key=openai_api_key) mcp_tool = MCPServerTool() tools = [mcp_tool] agent = initialize_agent(tools, llm, agent="zero-shot-react-description", verbose=True) # Example usage prompt = """ What is the current time on the Minecraft server? The input to this tool should be a json string with the command and arguments. """ # The agent will call the tool with an input like: # '{"command": "get_time", "arguments": {}}' # Assuming your _format_mcp_command formats it correctly. agent.run(prompt) ``` **Important Considerations** * **Security:** Be extremely careful about security. Never expose your MCP server directly to the internet without proper security measures. Sanitize all input to prevent command injection vulnerabilities. Consider using authentication and authorization. * **Error Handling:** Implement robust error handling to catch exceptions and provide informative error messages. * **Asynchronous Execution:** If you need asynchronous execution, you'll need to use `asyncio` and asynchronous socket operations. The `_arun` method in the example raises `NotImplementedError`. * **MCP Server Protocol:** The most important part is understanding and correctly implementing the communication protocol with your MCP server. Consult your server's documentation or source code for details. * **Langchain Agent Type:** The `zero-shot-react-description` agent is a good starting point, but you might need to experiment with different agent types to find one that works best for your use case. This comprehensive response should give you a solid foundation for building your MCP server tool adapter for Langchain. Remember to adapt the code to your specific needs and prioritize security. Good luck!

TreasuryOS

An AI-powered treasury management MCP server that enables users to perform commercial banking analysis, including cash flow forecasting, idle cash detection, and debt covenant monitoring. It provides tools to surface financial insights and optimize working capital through automated processing of bank and transaction data.

MCP Git Server

A Model Context Protocol server that enables LLMs to interact with Git repositories, providing tools to read, search, and manipulate Git repositories through commands like status, diff, commit, and branch operations.

Twelve Data MCP Server

Provides integration with Twelve Data API to access financial market data including historical time series, real-time quotes, and instrument metadata for stocks, forex pairs, and cryptocurrencies.

Cursor Agent Poisoning

A proof-of-concept attack that exploits Model Context Protocol (MCP) tool registration to achieve persistent agent poisoning in AI assistants like Cursor, embedding malicious instructions that persist across chat contexts without requiring tool execution.

Local Services MCP Server

A Multi-Agent Conversation Protocol Server that provides access to Google's Local Services API, enabling interaction with local service businesses information through natural language commands.

Crash MCP Server

Enables AI assistants to analyze Linux system crash dumps by automatically discovering dump files, matching kernels, and executing interactive crash analysis commands through the MCP protocol.

SAP SuccessFactors MCP Server by CData

SAP SuccessFactors MCP Server by CData

MCPBrowser

Fetches content from authenticated web pages by driving your signed-in Chrome/Edge browser via DevTools Protocol, automatically handling login redirects and reusing sessions across domains.

Tongyi Wanxiang MCP Server

A TypeScript-based Model Context Protocol server that enables large language models to directly invoke Alibaba Cloud's Tongyi Wanxiang text-to-image generation API.

MCPify

A dynamic proxy that converts OpenAPI Specification (OAS) endpoints into Message Communication Protocol (MCP) tools, allowing AI agents to use existing REST APIs as if they were native MCP tools without manual implementation.

SEO Audit MCP Server

Provides comprehensive technical SEO auditing tools including page analysis, site crawling, Lighthouse performance testing, and sitemap analysis, with specialized features for job board websites like JobPosting schema validation.