India Agriculture MCP Server

A Python MCP server for Indian agriculture workflows, built with FastMCP for Claude Desktop and other MCP-compatible clients.

Overview

This project exposes practical agriculture tools through the Model Context Protocol (MCP), so an AI client can call real functions instead of relying only on built-in knowledge.

The current server is focused on three common agriculture workflows:

• Farming weather lookup for Indian locations

• Crop advisory for common crops

• Mandi price reference lookup by crop and state

It is designed for local use with Claude Desktop through the stdio transport pattern supported by FastMCP.

Related MCP server: Maharashtra Transport MCP Server

Current tools

The server currently exposes these three MCP tools:

1. weather_for_farming(location: str)

Returns current weather conditions for a location and adds basic farming suitability guidance.

Example input

• Nagpur

• Pune,IN

• Delhi

What it includes

• Weather condition

• Temperature

• Feels-like temperature

• Humidity

• Wind speed

• Rain status if available

• Simple farming suitability notes

2. crop_advisory(crop: str, state: str = "")

Returns reference agronomy guidance for a crop.

Example input

• crop="soybean", state="Maharashtra"

• crop="cotton", state="Gujarat"

• crop="wheat", state="Punjab"

What it includes

• Season

• Soil type

• Water requirement

• Fertilizer guidance

• Common pests or diseases

• Practical cultivation tips

3. mandi_prices(crop: str, state: str)

Returns reference mandi price information for a crop in a specific Indian state.

Example input

• crop="soybean", state="Maharashtra"

• crop="cotton", state="Maharashtra"

What it includes

• Minimum price

• Maximum price

• Modal price

• Example market names

Current behavior and limitations

This server currently mixes live and reference-style outputs.

• Weather is a live current-weather snapshot.

• Crop advisory is reference agronomy guidance, not a field-specific expert bulletin.

• Mandi prices should be treated as reference market information unless explicitly connected to a verified live official source.

That means the project is useful for AI-assisted agriculture workflows, demos, and portfolio use, but important farm decisions should still be verified with local agronomy experts, soil tests, and official market data.

Tech stack

Project structure

mcp-india-server/
├── .env.example
├── .gitignore
├── README.md
├── requirements.txt
├── run_server.py
├── src/
│   ├── __init__.py
│   ├── server.py
│   └── tools/
│       ├── __init__.py
│       ├── crops.py
│       └── weather.py

Setup

1. Clone the repository

git clone https://github.com/Parikshit2005/-mcp-india-server.git
cd mcp-india-server

If your local folder name is different, use that folder name instead.

2. Create a virtual environment

python -m venv venv

3. Activate the virtual environment

Windows CMD

venv\Scripts\activate

If your project is on another drive, open CMD and run:

cd /d F:\mcp-india-server
venv\Scripts\activate

4. Install dependencies

pip install -r requirements.txt

5. Create the environment file

copy .env.example .env

Then add the required API keys and configuration values to .env.

6. Run the server

python run_server.py

Claude Desktop integration

Claude Desktop can connect to local MCP servers by launching them as subprocesses. FastMCP supports this pattern over stdio, which makes it a good fit for local tool usage.

Add an entry like this to your Claude Desktop MCP configuration:

{
  "mcpServers": {
    "india-mcp-server": {
      "command": "F:\\mcp-india-server\\venv\\Scripts\\python.exe",
      "args": ["F:\\mcp-india-server\\run_server.py"]
    }
  }
}

After saving the config:

1. Fully close Claude Desktop.

2. Reopen it.

3. Test prompts that should trigger the server tools.

Example prompts

Try prompts like these inside Claude Desktop:

• Give crop advisory for soybean in Maharashtra.

• Show mandi price for soybean in Maharashtra.

• Give farming weather for Nagpur,IN.

• What is the weather in Pune for farming today?

• Give crop advisory for cotton in Gujarat.

Implementation notes

The server is built with FastMCP and registers tools using the @mcp.tool() decorator. The current server code exposes:

• weather_for_farming

• crop_advisory

• mandi_prices

The server runs locally with:

mcp.run(transport="stdio")

This keeps the setup simple for local MCP client use.

Security

• Store secrets in .env, not in source files.

• Keep .env out of version control.

• Commit only .env.example with placeholder values.

• Review logs and debug output before sharing screenshots or demos.

Roadmap

Planned improvements:

• Add weather freshness display using API timestamp fields

• Improve mandi pricing with clearer official-source support

• Expand crop coverage and regional advisory detail

• Add tests for tool handlers and edge cases

• Add screenshots or demo recordings from Claude Desktop

• Add hosted deployment instructions for remote MCP usage

Use cases

This project is a good fit for:

• MCP learning and experimentation

• Claude Desktop custom tool integration

• Agriculture-focused assistant workflows

• Student and portfolio projects around AI tooling

• Rapid prototyping of India-specific utility assistants

Two tiny things to verify before using it:

If your repo folder is literally -mcp-india-server, keep that exact name in the clone and cd commands; otherwise use mcp-india-server.

If run_server.py actually imports src.server:mcp, your current wording is still fine, but the file path in Claude Desktop must exactly match your machine path.

License

Add a license before public release so reuse terms are clear.