🚀 MCP Agent & Server Ecosystem

A state-of-the-art demonstration of the Model Context Protocol (MCP), featuring autonomous agents, browser automation, and multi-server orchestration. This ecosystem leverage's Groq's high-performance inference to provide a seamless agentic experience.

🏗️ Architecture Overview

The system operates in two modes: an interactive CLI Agent and a standalone MCP Server. Both modes utilize the same core logic but differ in their entry points and orchestration.

1. CLI Agent Flow (app.py)

In this mode, the user interacts directly with a terminal-based agent.

graph TD
    subgraph "MCP Host Region (Application Space)"
        User["👤 User"] -->|Inputs| App["🖥️ app.py / CLI"]
        App -->|Initializes| Agent["🤖 MCPAgent"]
        Agent -->|Reasons with| LLM["🧠 Groq LLM"]
        Agent -->|Instantiates| Client["🔌 MCP Client"]
    end

    subgraph "Registry Region"
        Client -.->|Reads Registry Config| Config["📄 browser_mcp.json"]
    end

    subgraph "MCP Server Region (Child Processes)"
        Client -->|Connects To| PW["🌐 Playwright"]
        Client -->|Connects To| AB["🏠 Airbnb"]
        Client -->|Connects To| GA["⚙️ server.py (FastMCP)"]
    end

2. Prompting/Server Flow (server.py)

In this mode, the project acts as an MCP server itself, exposing a run_task tool to external clients.

graph TD
    subgraph "External Region"
        Ext["🌐 External MCP Client"] -->|Calls 'run_task'| GA["⚙️ server.py (FastMCP)"]
    end

    subgraph "MCP Host Region"
        GA -->|Initializes| Agent["🤖 MCPAgent"]
        Agent -->|Instantiates| Client["🔌 MCP Client"]
    end

    subgraph "Registry Region"
        Client -.->|Reads Config| Config["📄 browser_mcp.json"]
    end

    subgraph "Secondary MCP Server Region"
        Client -->|Delegates To| PW["🌐 Playwright"]
        Client -->|Delegates To| AB["🏠 Airbnb"]
    end

✨ Key Features

• ⚡ High-Performance Inference: Powered by Groq's llama-3.3-70b-versatile for near-instantaneous reasoning.

• 🌐 Autonomous Browser Control: Deep integration with Playwright for navigating and interacting with the web.

• 🔌 Flexible Server Protocol: Connects to any standard MCP server for extensible tool capabilities.

• 📂 State-Aware Memory: (In app.py) Maintains conversation state to handle complex, iterative requests.

• 🛠️ Custom Server Extension: Includes its own FastMCP server for wrapping agentic workflows as reusable tools.

📂 Project Structure

🛠️ Getting Started

1. Environment Setup

Ensure you have uv installed and a valid Groq API key.

# Clone the environment variables
echo "GROQ_API_KEY=your_key_here" > .env

2. Launch the Ecosystem

You can interact with the agent directly or run the custom server.

Start the Interactive Agent:

python app.py

Expose the Custom MCP Server:

python server.py

📖 Implementation Notes

The ecosystem is built on the mcp_use library, bridging LangChain components with the Model Context Protocol. The MCPAgent is configured with safety rails like max_steps to prevent infinite loops during autonomous execution.

Note: The previous mcp.json was detected as missing or redundant; all core configuration is now consolidated in browser_mcp.json.

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