AI_Agent_with_MCP

# MCP Server with Google ADK Project This project implements a Model Context Protocol (MCP) server using Google's Agent Development Kit (ADK) for building intelligent agents and tools. The system features multiple specialized agents that collaborate through a coordinator agent to handle different types of requests. [Details in Medium Post](https://zayedrais.medium.com/building-a-mcp-server-with-ai-tools-and-agents-a4cd71b05a36) ## Project Structure ``` ├── agents/ # Contains agent implementations │ ├── coordinator_agent.py # LLM-based intelligent request router │ ├── data_analysis_agent.py # Agent for analyzing data files with visualization │ ├── search_agent.py # Agent for web searches and information retrieval │ └── code_generator_agent.py # Agent for generating code based on descriptions ├── tools/ # Contains tool implementations │ ├── code_generator_tool.py # Generates code in various programming languages │ ├── data_analysis_tool.py # Analyzes data and creates visualizations │ ├── data_reader_tool.py # Reads data from various file formats │ ├── report_generator_tool.py # Generates formatted reports │ └── web_search_tool.py # Performs web searches ├── input_data/ # Directory for input data files (CSV, Excel) ├── analysis_output/plots/ # Generated data visualizations ├── reports/ # Generated analysis reports ├── generated_code/ # Generated code outputs ├── server.py # MCP Server implementation using FastMCP ├── requests_log.txt # Log of requests and responses └── README.md # This file ``` ## Prerequisites - Python 3.9+ - Groq and OpenRouter Key ## Working Flow Architecture  ## Setup 1. Clone this repository 2. Create a virtual environment: ``` python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate ``` 3. Install dependencies: ``` pip install -r requirements.txt ``` 4. Set up your Groq & Openrouter Key: add key in .env if available otherwise create file as ``` touch .env ``` add key ``` OPENROUTER_API_KEY="ADD_Your_Key" GROQ_API_KEY="ADD_Your_Key" ``` ## Running the MCP Server ``` python server.py ``` This will start the MCP server on `http://0.0.0.0:8080` ## Available Agents ### Coordinator Agent The coordinator agent uses LLM-based routing to direct requests to the most appropriate specialized agent. It analyzes the content of the request and determines which agent can best handle it. ### Search Agent Performs web searches and provides information on various topics. **Example Request:** ``` curl -X POST http://localhost:8080/ask -H "Content-Type: application/json" -d '{"query":"What is Model Context Protocol?"}' ``` ### Data Analysis Agent Analyzes data from various file formats (CSV, Excel) and generates reports with visualizations. **Example Request:** ``` curl -X POST http://localhost:8080/ask -H "Content-Type: application/json" -d '{"file_path": "sales_data.xlsx","query":"make a report for these data"}' ``` ### Code Generator Agent Generates code in various programming languages based on natural language descriptions. **Example Request:** ``` curl -X POST http://localhost:8080/ask -H "Content-Type: application/json" -d '{"query":"Python code for fibonacci series","language":"python"}' ``` ## API Endpoints - `/ask` - General endpoint that routes to the appropriate specialized agent - `/search` - Endpoint for direct web searches - `/analyze-data` - Endpoint for data analysis - `/generate-code` - Endpoint for code generation - `/chat/completions` - Chat completion endpoint for conversational interaction ## MCP Tools The server exposes the following MCP tools: - `ask` - Routes the user's query to the most appropriate agent - `search` - Searches the web for information - `analyze_data` - Analyzes data files and generates reports with visualizations - `generate_code` - Generates code based on natural language descriptions ## Extending This Project To add new agents: 1. Create a new file in the `agents/` directory 2. Implement the agent class with an async `process()` method 3. Update the coordinator agent to recognize and route to the new agent To add new tools: 1. Create a new file in the `tools/` directory 2. Implement the tool functionality with comprehensive docstrings 3. Import and use the tool in your agents