Agmarknet MCP Server

A Model Context Protocol (MCP) server that gives LLMs (like Claude) access to Indian agricultural commodity prices — daily wholesale min/max/modal rates across thousands of mandis (regulated markets) and hundreds of commodities.

Ask a model "Where were tomatoes cheapest in Pune last month?" and it answers from real market data.

Pick your implementation

The project lives on two branches with different data sources. They expose the same kind of tools; choose by your data needs.

TL;DR: Want to see the server work with no live API? Use main (it ships with sample data). Want real, reliable commodity data and trends? Use feat/ceda-api-migration.

Switch with git checkout main or git checkout feat/ceda-api-migration.

Related MCP server: Agricultural AI MCP Server

Setup

Install (same for both branches)

git clone https://github.com/Krishna-Baldwa/agmarket-mcp.git
cd agmarknet-mcp

git checkout feat/ceda-api-migration   # or: git checkout main

python -m venv .venv
source .venv/bin/activate
pip install -e .

Configure the API key

cp .env.example .env

On feat/ceda-api-migration (CEDA):

1. Get a free key from the CEDA Data Portal.

2. Set CEDA_API_KEY=<your key> in .env. Done — the server uses real data.

On main (data.gov.in):

• Out of the box it serves a small sample dataset (no key needed) so you can see the tools respond.

• To switch to real data.gov.in data:

  1. Get a free key at data.gov.in and set DATA_GOV_IN_API_KEY=<your key> in .env.

  2. In src/agmarknet_mcp/server.py, change the client to live mode:

     api_client = AgmarknetClient(use_mock=False)   # was use_mock=True

Connect to Claude Desktop

Add this to claude_desktop_config.json (macOS: ~/Library/Application Support/Claude/claude_desktop_config.json; The easiest way to get to it is from inside the app rather than hunting through folders: open Claude Desktop → Settings → Developer tab → click Edit Config. That opens the file directly (and creates it if it doesn't exist yet).), using the env var for your branch:

{
  "mcpServers": {
    "agmarknet": {
      "command": "/absolute/path/to/agmarknet-mcp/.venv/bin/python",
      "args": ["-m", "agmarknet_mcp.server"],
      "env": {
        "CEDA_API_KEY": "your_api_key_here"
      }
    }
  }
}

(On main, use "DATA_GOV_IN_API_KEY" instead — or omit env to run on sample data.)

Restart Claude Desktop and ask:

• "Compare tomato prices across mandis in Pune district."

• "What's the 60-day onion price trend in Maharashtra?" (CEDA branch)

Test without an LLM

npx @modelcontextprotocol/inspector python -m agmarknet_mcp.server

Tools

feat/ceda-api-migration (CEDA — real historical data)

Tools accept names, not numeric ids (see Design). state is required; district narrows to individual mandis (omit for a state-wide aggregate).

main (data.gov.in + sample fallback)

Design (CEDA branch)

The CEDA API is id-based: commodities, states, districts, and markets are all numeric ids, and /prices returns ids, not names. LLMs and humans think in names, so the server's core job is translation:

• Name → ID resolution. Tools accept "Tomato", "Maharashtra", "Pune"; the client resolves them to ids (case-insensitive, unique-substring matching) before calling the API, and raises a clear error for unknown/ambiguous names so the model can self-correct.

• ID → name enrichment. Raw price rows (which carry only market_id) are translated back into readable records before reaching the model.

• Cached reference data. Commodities, geographies, and markets are fetched once and reused.

• Tolerant validation. pydantic validates every response. The API changes shape by granularity (state-level queries omit district_id/market_id), which the models handle explicitly.

Module layout

• server.py — FastMCP server: defines the tools and handles the stdio/JSON-RPC lifecycle.

• api.py — the API client (CedaClient on CEDA; AgmarknetClient with sample fallback on main).

• models.py — pydantic models for the API's raw shapes and the domain objects the tools return.

Data sources

• main: Directorate of Marketing & Inspection (DMI), Govt. of India, via data.gov.in.

• CEDA branch: CEDA Agri Market Data, "Centre for Economic Data & Analysis, Ashoka University" — built on the same Agmarknet data, 2000–present. Free to download, display or include the data in other products for non-commercial purposes.

License

MIT