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.

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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.

	main	feat/ceda-api-migration
Data source	data.gov.in Agmarknet	CEDA Agri Market API (Ashoka University)
API key	DATA_GOV_IN_API_KEY (free)	CEDA_API_KEY (free)
Without a key	Serves a small sample dataset so the tools work out of the box — for flavour only, not live prices	Will not run — a key is required
To use real data	Add the key to .env and set use_mock=False in server.py	Add the key to .env
Data coverage	Current day only (when the live API is reachable)	Historical, 2000–present (lags ~a few months)
Price trends	✗	✅ get_price_trend over a date range
Reliability	data.gov.in is frequently down / WAF-blocked	Stable
Tools	5	6
Best for	Trying the tools instantly on sample data	Real analysis — trends and accurate prices

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.

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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

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Tools

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

Tool	What it does
get_commodity_price(commodity, state, district?, date?)	Min/max/modal prices — latest available date, or a specific YYYY-MM-DD.
compare_markets(commodity, state, district, top_n=10)	Rank a district's mandis cheapest-first.
get_price_summary(commodity, state, district?)	Average / cheapest / dearest on the latest date.
get_price_trend(commodity, state, district?, days=30)	Daily-average price, window average, high/low, and % change over N days.
list_commodities(search?)	List tracked commodities, optionally filtered.
list_markets(commodity, state, district)	List the mandis reporting a commodity in a district.

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)

Tool	What it does
get_commodity_price(commodity, state?, district?, market?)	Daily min/max/modal price for a commodity.
compare_markets(commodity, state?, top_n=10)	Rank markets cheapest-first.
get_price_summary(commodity, state?)	Average / cheapest / dearest across markets.
list_commodities(search?)	List commodities in the current dataset.
list_markets(state?, commodity?)	List reporting mandis.

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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.

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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