Agentic Swiggy MCP Server

A highly modular, non-deterministic agentic Model Context Protocol (MCP) server that mimics a food delivery assistant (like Swiggy). Built with LangGraph, FastMCP, and ChromaDB, this server leverages an intelligent ReAct agent to dynamically route tasks, answer questions, provide recommendations, and safely execute food orders using a Human-in-the-Loop (HITL) workflow.

Core Features

• Non-Deterministic ReAct Agent: The agent dynamically decides which tools to use based on the conversational context without hardcoded workflows.

• Human-In-The-Loop (HITL): Order placements are securely intercepted. The agent pauses execution to ask for human authorization before finalizing any real transactions.

• Multi-Format RAG Ingestion: A decoupled ingestion pipeline that universally parses .json, .csv, .md, .txt, and .pdf files into a local ChromaDB vector store.

• Contextual Recommendations: Recommends the top 3 food items dynamically evaluated against the user's current geographic location and time of day.

• Model Context Protocol (MCP): Exposes the agent's capabilities as standardized MCP tools, making them discoverable and usable by modern LLM clients (like Claude Desktop).

• 100% Local Embeddings: Built-in support for HuggingFace sentence-transformers for cost-free, offline vector embeddings.

Related MCP server: Food402

Project Structure

swiggy_agent/
├── data/
│   ├── restaurants_menu.json        # Restaurant catalog & timings
│   ├── order_history.json           # Past & active order records
│   ├── food_reviews.csv             # Customer reviews & sentiment
│   └── platform_policies.md         # Packaging & late-night guidelines
├── src/
│   ├── ingestion.py                 # Multi-format universal data ingestor
│   ├── tools/
│   │   ├── __init__.py
│   │   ├── order_tool.py            # Tool 1: Order Food (HITL sensitive)
│   │   ├── recommendation_tool.py   # Tool 2: Geo/Time Recommendations
│   │   ├── history_tool.py          # Tool 3: Order tracking & history
│   │   └── rag_tool.py              # Tool 4: Review retrieval RAG
│   ├── agent.py                     # Non-deterministic LangGraph agent + HITL
│   └── mcp_server.py                # FastMCP Server exposing tools
├── requirements.txt
├── .env                             # Environment variables (API Keys)
└── main.py                          # Entry point for interactive CLI testing

Setup & Installation

1. Install Dependencies

Ensure you have Python 3.10+ installed. Install the required packages:

pip install -r requirements.txt
# Alternatively, install manually:
pip install mcp langchain langchain-openai langchain-huggingface langchain-chroma langgraph chromadb sentence-transformers python-dotenv

2. Configure Environment Variables

Create a .env file in the root directory and add your LLM API keys (e.g., Google Gemini, OpenAI, or Groq):

# Example using Google Gemini (Recommended for free tier)
GEMINI_API_KEY=your_api_key_here

# Example using OpenAI (If applicable)
# OPENAI_API_KEY=your_api_key_here

3. Initialize the Vector Database

Before running the agent, ingest the synthetic data to build the local ChromaDB vector store:

python src/ingestion.py

Usage

Option 1: Run the Interactive Agent Demo

Test the LangGraph agent and the Human-in-the-Loop order workflow via the terminal:

python main.py

Try a prompt like: "Hi, I'm USR_500 in Koramangala at 21:00. What's good to eat around here? Check reviews for what people say, and if it's rated well, order 1 portion to 5th Block."

Option 2: Start the MCP Server

To expose the tools to an MCP-compatible client (like Claude Desktop or an external app):

python src/mcp_server.py

Tools Reference

License

MIT License