Discover Awesome MCP Servers

findevil-agent

Enables autonomous digital forensics and incident response through 21 typed forensic tools covering disk, memory, registry, network, timeline, carving, and patterns, integrated with AI-driven reasoning and self-correction.

Todoist MCP Server

Enables AI assistants to interact with Todoist tasks and projects through natural language. Supports comprehensive task management including creating, updating, completing tasks, managing projects, and filtering by various criteria.

Earth Engine MCP Server

Enables satellite imagery analysis through Google Earth Engine, allowing users to search datasets, calculate vegetation indices like NDVI, filter collections by location and date, and export imagery to cloud storage. Supports major satellite datasets including Sentinel-2, Landsat, and MODIS for applications like agriculture monitoring and deforestation tracking.

Nexus MCP Server

Enables hybrid web search and intelligent content extraction, combining semantic search with documentation-optimized reading that strips noise and returns clean, token-efficient context for AI agents.

kickstart-mcp

🚀 Kickstart-mcp là hướng dẫn sử dụng mcp, tạo máy chủ/máy khách mcp của riêng bạn. Chúng tôi sẽ hướng dẫn bạn từng bước trong hành trình MCP của bạn.

medwriter-mcp

Medical Writer's AI Toolkit — 33 expert prompts for pharma medical writing as MCP tools

Grimoire

A local security knowledge base that indexes documentation like CVEs and CWEs using hybrid keyword and semantic search. It enables LLM agents to query indexed materials via MCP for accurate, offline retrieval during security audits and code reviews.

obsidian-cli-rest

Enables programmatic control of Obsidian vaults via REST API and MCP server for automation and AI assistant integration.

Etsy MCP Server

Exposes a subset of the Etsy API through the Model Context Protocol, enabling MCP clients to retrieve shop data and manage listings.

Garmin Connect MCP Server

Connects Garmin Connect data to MCP-compatible clients, providing access to fitness activities, health metrics, and training plans. It supports advanced features like headless 2FA and automated MFA retrieval to enable seamless health data interaction through natural language.

Supabase MCP Server

Connects AI assistants to Supabase projects, enabling them to manage tables, query data, deploy Edge Functions, handle migrations, and access project resources through natural language commands.

thermal-mcp-server

A physics engine for liquid-cooled GPU systems, exposed as an AI-callable MCP server. Enables thermal analysis, coolant comparison, flow optimization, and rack-level sizing via natural language queries.

vSphere MCP Server

Enables AI agents to manage VMware vSphere virtual infrastructure through comprehensive operations including VM power control, snapshot management, resource monitoring, performance analytics, and bulk operations with built-in safety confirmations for destructive actions.

Interactive Feedback MCP

A Model Context Protocol server that enables AI assistants to request user feedback at critical points during interactions, improving communication and reducing unnecessary tool calls.

litra-paper-search-mcp

AI-powered academic paper search MCP server with relevance scoring, summarization, author search, and credit checking. Enables users to search papers naturally and get scored results.

meal-tracker-mcp

Tracks meals for three people with cost calculation, deployable on Render and connectable to ChatGPT.

YaVendió Tools

An MCP-based messaging system that allows AI systems to interact with various messaging platforms through standardized tools for sending text, images, documents, buttons, and alerts.

Model Context Protocol (MCP)

Okay, I understand. You want a working pattern (a design pattern or architectural approach) for building SSE (Server-Sent Events) based MCP (Message Channel Protocol) clients and servers, leveraging the Gemini LLM (Large Language Model). Here's a breakdown of a potential working pattern, along with explanations and considerations: **Core Idea:** The pattern aims to use SSE for real-time communication between MCP clients and servers, with Gemini LLM playing a role in processing or generating messages within this communication flow. This could involve: * **LLM-Enhanced Message Generation:** The server uses Gemini to generate dynamic and context-aware messages sent to the client via SSE. * **LLM-Powered Client Interaction:** The client uses Gemini to understand and respond to SSE messages, potentially generating new messages to send back to the server (perhaps via a separate channel like a standard HTTP POST). * **LLM-Driven Data Enrichment:** The server uses Gemini to enrich data before sending it to the client via SSE, providing more insightful or personalized information. **Pattern Name:** LLM-Augmented Real-Time Messaging (or something similar) **Components:** 1. **MCP Client:** * **SSE Connection Handler:** Establishes and maintains the SSE connection to the server. Handles incoming SSE events. * **Message Parser:** Parses the data received in SSE events. This might involve JSON parsing, or other formats depending on your MCP implementation. * **Gemini Integration (Optional):** * **Message Understanding:** Uses Gemini to understand the meaning and intent of incoming SSE messages. * **Response Generation:** Uses Gemini to generate appropriate responses to the server, potentially based on the content of the SSE messages and the user's current context. * **Data Presentation:** Uses Gemini to format and present the data received via SSE in a user-friendly way. * **User Interface (UI):** Displays information to the user and allows them to interact with the system. * **Request Sender (Optional):** If the client needs to send data back to the server (e.g., user input, acknowledgements), this component handles sending HTTP requests (e.g., POST, PUT) to the server. 2. **MCP Server:** * **SSE Endpoint:** An HTTP endpoint that serves SSE events. * **Message Generator:** Creates the messages to be sent to the client via SSE. * **Gemini Integration (Crucial):** * **Message Generation:** Uses Gemini to generate dynamic and context-aware messages. This could involve: * Generating personalized recommendations. * Creating summaries of data. * Crafting engaging notifications. * Adapting messages based on user behavior. * **Data Enrichment:** Uses Gemini to enrich data before sending it to the client. For example, adding sentiment analysis, topic extraction, or contextual information. * **Request Processing (Optional):** Handles incoming HTTP requests from the client (e.g., POST requests with user input). May use Gemini to process or understand these requests. * **Data Source:** The source of the data that is being sent to the client. This could be a database, an API, or other data sources. * **Event Stream:** A mechanism for triggering the generation of SSE events. This could be based on database changes, external events, or scheduled tasks. **Communication Flow:** 1. **Client Connection:** The client establishes an SSE connection to the server's SSE endpoint. 2. **Server Event Trigger:** An event occurs on the server (e.g., data update, timer expiry). 3. **Message Generation (Server):** The server's message generator, potentially using Gemini, creates a message to be sent to the client. Gemini might be used to personalize the message, summarize data, or add context. 4. **SSE Event Transmission:** The server sends the message to the client as an SSE event. 5. **Client Event Reception:** The client receives the SSE event. 6. **Message Parsing (Client):** The client parses the message data. 7. **Gemini Processing (Optional Client):** The client, optionally, uses Gemini to understand the message and generate a response. 8. **UI Update (Client):** The client updates the UI to display the information to the user. 9. **Client Request (Optional):** The client, optionally, sends a request back to the server (e.g., user input, acknowledgement). 10. **Server Request Processing (Optional):** The server processes the client's request, potentially using Gemini to understand the request. **Example Scenario: Real-Time Stock Updates with LLM-Powered Insights** * **MCP Client:** A stock trading application. * **MCP Server:** A server providing real-time stock data. * **SSE:** Used to push stock price updates to the client. * **Gemini (Server):** Used to generate insights and summaries about the stock price movements. For example: * "Stock XYZ is up 5% today, driven by positive news about their new product launch. Analysts predict further gains in the short term." * "Warning: Stock ABC has experienced a sudden drop of 10% in the last hour. This may be due to a negative earnings report." * **Gemini (Client - Optional):** Could be used to allow the user to ask questions about the stock data, such as "Why is stock XYZ going up?" and receive a Gemini-generated answer based on the SSE data and external information. **Code Snippets (Illustrative - Python with Flask and `sse_starlette`):** **Server (Python/Flask):** ```python from flask import Flask, Response, request from sse_starlette.sse import EventSourceResponse import google.generativeai as genai import os app = Flask(__name__) # Configure Gemini (replace with your API key) GOOGLE_API_KEY = os.environ.get("GOOGLE_API_KEY") genai.configure(api_key=GOOGLE_API_KEY) model = genai.GenerativeModel('gemini-pro') # Mock stock data (replace with your actual data source) stock_prices = {"XYZ": 100.00, "ABC": 50.00} async def stock_price_stream(): while True: # Simulate stock price changes stock_prices["XYZ"] += (random.random() - 0.5) * 2 # +/- 1 stock_prices["ABC"] -= (random.random() - 0.5) * 1 # +/- 0.5 # Generate LLM-powered insight prompt = f"Summarize the current stock prices: XYZ is {stock_prices['XYZ']:.2f}, ABC is {stock_prices['ABC']:.2f}." response = model.generate_content(prompt) insight = response.text event_data = { "XYZ": stock_prices["XYZ"], "ABC": stock_prices["ABC"], "insight": insight } yield { "event": "stock_update", "data": json.dumps(event_data) } await asyncio.sleep(5) # Send updates every 5 seconds @app.route('/stream') async def stream(): return EventSourceResponse(stock_price_stream()) if __name__ == '__main__': import random import asyncio import json app.run(debug=True, port=5000) ``` **Client (JavaScript):** ```html <!DOCTYPE html> <html> <head> <title>Stock Updates</title> </head> <body> <h1>Stock Prices</h1> <div id="stock-data"></div> <script> const eventSource = new EventSource('/stream'); eventSource.onmessage = function(event) { console.log("Raw event data:", event.data); // Log the raw data try { const data = JSON.parse(event.data); console.log("Parsed data:", data); // Log the parsed data const stockDataDiv = document.getElementById('stock-data'); stockDataDiv.innerHTML = ` XYZ: ${data.XYZ.toFixed(2)}<br> ABC: ${data.ABC.toFixed(2)}<br> Insight: ${data.insight} `; } catch (error) { console.error("Error parsing JSON:", error); console.error("Event data that caused the error:", event.data); } }; eventSource.onerror = function(error) { console.error("SSE error:", error); }; </script> </body> </html> ``` **Key Considerations:** * **Gemini API Usage:** Be mindful of Gemini's API usage limits and costs. Implement caching and rate limiting to avoid exceeding your quota. * **Error Handling:** Implement robust error handling on both the client and server. Handle connection errors, API errors, and data parsing errors gracefully. * **Security:** Secure your SSE endpoint. Use authentication and authorization to prevent unauthorized access. Sanitize any user input before sending it to Gemini. * **Data Format:** Choose a data format for SSE events that is efficient and easy to parse (e.g., JSON). * **Scalability:** Consider the scalability of your SSE server. Use a scalable SSE library or framework. You might need to use a message queue (e.g., RabbitMQ, Kafka) to handle a large number of clients. * **Real-time Requirements:** SSE is near real-time, but it's not guaranteed to be perfectly real-time. If you need true real-time communication, consider using WebSockets. * **Complexity:** Integrating an LLM adds complexity. Carefully consider whether the benefits of using Gemini outweigh the added complexity. * **Context Management:** For more complex interactions, you'll need to manage the context of the conversation between the client and server. This could involve storing conversation history or using Gemini's context management features. * **Prompt Engineering:** The quality of Gemini's output depends heavily on the prompts you provide. Experiment with different prompts to find the ones that produce the best results. **Benefits of this Pattern:** * **Real-time Updates:** SSE provides real-time updates to the client. * **LLM-Powered Intelligence:** Gemini can be used to generate dynamic, personalized, and insightful messages. * **Improved User Experience:** The combination of real-time updates and LLM-powered intelligence can lead to a more engaging and informative user experience. * **Flexibility:** The pattern is flexible and can be adapted to a variety of use cases. **Alternatives:** * **WebSockets:** WebSockets provide full-duplex communication, which can be useful if the client needs to send a lot of data back to the server. * **Long Polling:** Long polling is a simpler alternative to SSE, but it is less efficient. * **Traditional HTTP Requests:** For applications that don't require real-time updates, traditional HTTP requests may be sufficient. This detailed explanation and code example should give you a solid foundation for building SSE-based MCP clients and servers using Gemini LLM. Remember to adapt the pattern to your specific needs and requirements. Good luck!

manufacturing-defect-mcp

MCP server for manufacturing defect investigation using knowledge graphs and graph analytics. Enables LLM agents to retrieve context, find similar cases, and generate investigation reports from manufacturing data.

mcp-minecraft-forge

MCP server for Forge Minecraft modding documentation. Gives AI assistants direct access to Forge docs with structured search results.

Skills Manager MCP Server

A visual web interface and MCP server for managing Claude MCP Skills, enabling users to create, edit, browse, and load skill sets through a browser UI or directly from Claude Desktop.

expenses-tracker-mcp

Enables read-only SQL queries and transaction classification tools for local expense tracking, including listing, filtering, and bulk account assignment with dry-run mode.

Oomol Connect MCP Server

Enables integration with Oomol Connect for executing tasks, managing blocks and packages, and uploading files. Supports task execution with real-time progress monitoring and intelligent polling across audio, video, and other processing workflows.

manifold-market-api

Wraps the Manifold Markets public REST API into an MCP server, providing 40 tools for querying markets, users, groups, bets, comments, and more.

MCP Filesystem Server

Một máy chủ Giao thức Ngữ cảnh Mô hình (Model Context Protocol) cung cấp tương tác an toàn và thông minh với các tệp và hệ thống tệp, cung cấp khả năng quản lý ngữ cảnh thông minh và các hoạt động tiết kiệm token để làm việc với các tệp lớn và cấu trúc thư mục phức tạp.

Gov Data MCP

MCP server with 562 tools accessing 114 government data APIs covering economic, health, education, energy, and more from federal, state, and international sources.

agriculture-mcp-server

MCP server for agriculture and farming data. 8 tools: soil conditions (temperature, moisture), crop weather forecasts, historical climate data (NASA POWER, since 1981), global agriculture statistics (World Bank, 20+ indicators), and food product database (Open Food Facts, 3M+ products). All APIs free, no keys required.

ci-sentinel

Security auditor for CI ecosystems that detects supply-chain and injection flaws in CI/CD configuration files across seven CI platforms, providing taint analysis, remediation, and compliance scoring.

Toast MCP Server

Một máy chủ MCP hiển thị thông báo trên màn hình nền trên Windows 10 và macOS, tương thích với VSCode Cline và hỗ trợ các tham số thông báo có thể tùy chỉnh.

Music MCP Server

Enables music library management and Chromecast playback control via LM Studio, with 21 tools for search, playback, navigation, and queue management.