Maharashtra Medicine Purchase — FastMCP Server

AI-powered MCP server that lets Claude (or any MCP client) analyse Maharashtra wholesale medicine purchase data through 10 focused tools.

Project Structure

medicine_mcp_server/
├── server.py                         # MCP server (all tools)
├── data/
│   └── maharashtra_wholesale_medicine_purchase.csv
├── pyproject.toml
└── README.md

Quick Start

1. Install dependencies

pip install fastmcp pandas google-generativeai

2. Set your Gemini API key (needed only for natural_language_query)

export GEMINI_API_KEY=AIza...
# or: export GOOGLE_API_KEY=AIza...

Get a free key at https://aistudio.google.com/app/apikey

3. Run locally (stdio transport — Claude Desktop / mcp-remote)

python server.py

4. Run as HTTP server (SSE transport — Azure App Service / any HTTP host)

# In server.py, change the last line to:
mcp.run(transport="sse", host="0.0.0.0", port=8000)

Or pass via CLI:

fastmcp run server.py --transport sse --host 0.0.0.0 --port 8000

Claude Desktop Config (claude_desktop_config.json)

{
  "mcpServers": {
    "medicine": {
      "command": "python",
      "args": ["/path/to/medicine_mcp_server/server.py"],
      "env": {
        "GEMINI_API_KEY": "AIza..."
      }
    }
  }
}

Azure App Service Deployment

1. Push the project to your Azure App Service.

2. Set ANTHROPIC_API_KEY as an Application Setting.

3. Set startup command:

   fastmcp run server.py --transport sse --host 0.0.0.0 --port 8000

4. In Claude Desktop / mcp-remote, point to:

   https://<your-app>.azurewebsites.net/sse

Available Tools

Example Queries (Natural Language Tool)

• "Which supplier sold the most Schedule H drugs?"

• "What is the total GST paid by Ganesh Medical Store?"

• "List all Cipla products purchased in April 2024."

• "Which medicines expire before December 2025?"

• "Show top 5 products by total spend."

• "Which invoices had the highest discount percentage?"

• "What is the average MRP of Schedule X drugs?"

Extending to a Larger Dataset

The CSV path is set in server.py:

CSV_PATH = os.path.join(os.path.dirname(__file__), "data", "maharashtra_wholesale_medicine_purchase.csv")

Replace the CSV with a larger file using the same column schema and restart the server. All tools will automatically work on the new data. For datasets

100k rows consider loading into Azure Cognitive Search and replacing the _load_df() function with search-index queries.