Discover Awesome MCP Servers

Simple MCP Server Example

A simple example of a Model Context Protocol Server implementation

Git

Tools to read, search, and manipulate Git repositories

Google Drive & Sheets MCP Server

A Model Context Protocol (MCP) server built in Rust for interacting with Google Drive and Google Sheets.

simple_mcp_server

Test Simple MCP server

ChatSum

Okay, I understand. Since I don't have access to your personal chat messages, I can't query and summarize them directly. That would be a privacy violation. However, I can explain how you *could* do this if you had access to your chat data and the right tools: **Here's a general approach you could take:** 1. **Access Your Chat Data:** This is the trickiest part. How you do this depends entirely on the chat platform you're using (e.g., WhatsApp, Facebook Messenger, Slack, Discord, etc.). Most platforms have some way to export your chat history, but the format varies. Look for options like: * "Export Chat" * "Download Your Information" * "Data Export" The exported data is often in a format like: * `.txt` (plain text - often difficult to parse) * `.json` (structured data - easier to parse programmatically) * `.csv` (comma-separated values - good for spreadsheets) * `.html` (web page format - can be parsed) 2. **Parse the Data:** You'll need to write a script (using a programming language like Python) to read the exported data and extract the relevant information: * **Sender:** Who sent the message? * **Timestamp:** When was the message sent? * **Message Content:** The actual text of the message. The complexity of this step depends on the format of the exported data. JSON is generally the easiest to work with. 3. **Query the Data:** Once you have the data parsed into a usable format (e.g., a list of dictionaries or a database), you can start querying it. Here are some examples of queries you might want to perform: * **Keyword Search:** Find all messages containing a specific word or phrase (e.g., "meeting," "project deadline," "restaurant recommendation"). * **Sender-Specific:** Find all messages from a particular person. * **Date Range:** Find all messages within a specific date range. * **Time of Day:** Find messages sent during specific hours. * **Combination Queries:** Find messages from a specific person containing a specific keyword within a certain date range. You can use Python's string manipulation functions, regular expressions, or database queries (if you've loaded the data into a database) to perform these searches. 4. **Summarize the Results:** After you've queried the data, you'll want to summarize the results. Here are some ways to do that: * **Frequency Analysis:** Count the number of times certain keywords appear in the results. * **Topic Modeling:** Use natural language processing (NLP) techniques to identify the main topics discussed in the messages. Libraries like `gensim` in Python can help with this. * **Sentiment Analysis:** Determine the overall sentiment (positive, negative, neutral) of the messages. Libraries like `nltk` or `spaCy` in Python can help with this. * **Simple Summarization:** Extract the most important sentences from the messages. There are Python libraries that can do this automatically. * **Manual Summarization:** Read through the results and write your own summary. **Example (Conceptual Python Code):** ```python # This is just a simplified example. The actual code will depend on your data format. import json def analyze_chat_data(filepath, search_term): with open(filepath, 'r', encoding='utf-8') as f: data = json.load(f) # Assuming JSON format messages = data['messages'] # Assuming 'messages' is the key for the list of messages matching_messages = [] for message in messages: if search_term.lower() in message['text'].lower(): matching_messages.append(message) print(f"Found {len(matching_messages)} messages containing '{search_term}':") for message in matching_messages: print(f" {message['sender']}: {message['text']} ({message['timestamp']})") # Add summarization logic here (e.g., count keyword frequency) # Example usage (replace with your actual file path and search term) analyze_chat_data("my_chat_data.json", "project deadline") ``` **Important Considerations:** * **Privacy:** Be extremely careful when handling your chat data. It contains personal information. Don't share it with anyone you don't trust. * **Data Format:** The biggest challenge is usually parsing the data from the format it's exported in. * **NLP Libraries:** For more advanced summarization and analysis, you'll want to learn how to use NLP libraries like `nltk`, `spaCy`, `gensim`, and `transformers` in Python. * **Ethical Considerations:** Be mindful of the ethical implications of analyzing your chat data, especially if it involves other people. Let me know if you have any more questions about specific aspects of this process. I can provide more detailed guidance if you can tell me what chat platform you're using and what kind of analysis you want to perform.

gameanalytics-server MCP Server

GameAnalytics MCP server for Model Context Protocol integration

Confluence MCP Server

Confluence MCP server providing API tools for Atlassian Confluence operations including page management, space handling, and content search with built-in rate limiting and error handling.

Perplexity MCP Server

An MCP server for the Perplexity for use with Claude Code and Claude Desktop, giving you enhanced search and reasoning capabilties.

PubMed MCP Server

🔍 Cho phép các trợ lý AI tìm kiếm, truy cập và phân tích các bài báo trên PubMed thông qua một giao diện MCP đơn giản.

MCP Servers for Cursor AI - README

Tôi đã thu thập rất nhiều thông tin về các máy chủ MCP (Model Context Protocol), tích hợp với Cursor AI và Claude Desktop. Bằng cách đó, bạn có thể thêm thư mục này vào IDE ưa thích của mình để nó có thông tin lập chỉ mục theo ngữ cảnh, giúp bạn nắm bắt cách tạo máy chủ MCP một cách chính xác.

Airbnb MCP Server (Enhanced)

Enhanced MCP server for Airbnb search and listing details

AI Federation Network

Việc triển khai này tuân theo đặc tả MCP chính thức, bao gồm việc đóng khung tin nhắn thích hợp, triển khai lớp vận chuyển và quản lý vòng đời giao thức hoàn chỉnh. Nó cung cấp nền tảng để xây dựng các hệ thống MCP liên kết có thể mở rộng trên nhiều máy chủ trong khi vẫn duy trì các yêu cầu về bảo mật và tiêu chuẩn hóa.

aoirint_mcping_server

Headless status monitor with HTTP JSON API for Minecraft Bedrock/Java server

MCP Server Readability Parser (Python / FastMCP)

Gương của

GitHub MCP (Mission Control Protocol) Servers

Một kho lưu trữ trình bày sự tích hợp giữa Cursor IDE và Máy chủ GitHub MCP (Giao thức Kiểm soát Nhiệm vụ).

Agentify Components

These are the components that a user can download to create MCP servers on the fly

MCP Gateway, Server, and Client

An MCP stdio to HTTP SSE transport gateway with example server and MCP client

LocalMind

LocalMind is an local LLM Chat App fully compatible with the Model Context Protocol. It uses Azure OpenAI as a LLM backend and you can connect it to all MCP Servers out there.

MCP Google Calendar Server

Triển khai máy chủ Giao thức Ngữ cảnh Mô hình (MCP) để tích hợp Google Calendar. Tạo và quản lý các sự kiện lịch trực tiếp thông qua Claude hoặc các trợ lý AI khác.

@enemyrr/mcp-server-pagespeed

Gương của

mcp-sql

MCP server to give client the ability to access SQL databases (MySQL and PostgreSQL supported)

Slack

Channel management and messaging capabilities

MCP-Servers

MCP Servers 倉庫

MCP Explorer UI

an open-source web application for exploring and visualizing the MCP Servers

mcp server hello world with java

A repository created using the MCP server

Tracxn MCP Server

Triển khai máy chủ Giao thức Điều khiển Mô hình (MCP) để tương tác với API Tracxn.