MCP (Model Context Protocol) Server: Intelligent Conversational Platform

Overview

MCP (Model Context Protocol) is a sophisticated AI-powered server designed to provide intelligent, context-aware conversational capabilities. This standalone server leverages multiple LLM providers (OpenAI, Anthropic, and Google Gemini), FastAPI, and Pyppeteer for web browsing capabilities to deliver nuanced, contextually relevant responses across various business domains.

Note: This repository contains only the MCP server implementation. While frontend examples are provided in the documentation for illustrative purposes, the actual frontend implementation is not included in this repository. The MCP server is designed to be integrated with any frontend through its RESTful API.

Key Features

- 🤖 Role-based AI advisor system with customizable instructions and tones - 🧠 Semantic memory management with vector similarity search - 🌊 Real-time streaming responses for improved user experience - 🌐 Integrated web browsing capabilities for AI-assisted research - 🔄 Dynamic context switching based on conversation triggers - 📝 Enhanced markdown formatting for professional-looking content - 🖼️ Multi-modal context support for processing images and other media - 🔍 Advanced role search and filtering by keywords, domains, and tone - 🔗 Advanced memory features with tagging, sharing, and inheritance - 🔄 Multiple LLM provider support (OpenAI, Anthropic, Google Gemini)

Technology Stack

- Backend: Python with asyncio - Web Framework: FastAPI - AI Models: - OpenAI GPT-4o-mini - Anthropic Claude models - Google Gemini models - Browser Automation: Pyppeteer (Python port of Puppeteer) - API Documentation: Swagger UI via FastAPI

Setup and Installation

Prerequisites

- Python 3.11+ - OpenAI API key - Git (for cloning the repository)

Installation Steps

1. Clone the repository 2. Create a virtual environment: python -m venv venv 3. Activate the virtual environment: - Windows: venv\Scripts\activate - macOS/Linux: source venv/bin/activate 4. Install dependencies: pip install -r requirements.txt 5. Configure environment variables (see below) 6. Run the server: python -m app.main 7. Access the API documentation at http://localhost:8000/docs

Configuration

Create a .env file based on .env.example with the following variables:

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