Guru-ragul/KABO-Indian-Nutrition-Index MCP-server

An MCP server exposing the Indian food nutrition data behind kabo.co.in as
tools an AI agent can call directly. Every response carries a source URL, so
an agent answering a nutrition question gets a citable answer and the reader
gets a link back.
Why this rather than more articles. A blog post can be copied in an
afternoon. A maintained data service that agents connect to cannot. This is
the one asset in the SEO programme that competitors cannot replicate by
rewriting content.
## What it does
| Tool | Returns |
| lookupfood(food, nutrient?) | Every nutrient entry for an Indian food, per 100 g and per katori, each with its source URL |
| comparefoods(foods[], nutrient?) | 2–8 foods side by side on one nutrient (default protein) |
| proteintarget(weightkg, age) | Daily protein target in grams, plus the per-meal threshold |
| proteincost(budgetinr) | Cost per 10 g of protein, ranked cheapest first |
| searchguides(query, limit) | Searches 1,124 nutrition guides, returns titles and URLs |
| listproducts() | KABO's own shakes: per-serving nutrition from each flavour's own panel, ratings |
| getproduct(product) | One KABO product: composition quoted from its page, suitability (incl. not Jain-suitable), live price and stock per pack, price per serving, cart link and UCP checkout profile |
| compareproducts(products?) | KABO products side by side with live price per serving |
Data: 59 foods, 674 nutrition values, 1,124 guides (protein, fibre, iron, calcium), from ICMR-NIN
composition tables and the PROT-AGE/ESPEN protein recommendations.
## Running it
No dependencies. Standard library only, Python 3.8+.
bash
python3 kabomcpserver.py
### Claude Desktop
Add to claudedesktopconfig.json:
json
"mcpServers": {
"kabo-nutrition": {
"command": "python3",
"args": ["/absolute/path/to/mcp/kabomcpserver.py"]
Cursor, Cline and other MCP clients use the same command/args shape.
### Test it without a client
bash
printf '%s\n' \
'{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2025-06-18","capabilities":{},"clientInfo":{"name":"t","version":"1"}}}' \
'{"jsonrpc":"2.0","id":2,"method":"tools/list"}' \
'{"jsonrpc":"2.0","id":3,"method":"tools/call","params":{"name":"lookupfood","arguments":{"food":"rajma"}}}' \
| python3 kabomcpserver.py
## Files
kabomcpserver.py the server, dependency-free JSON-RPC over stdio
kabofooddata.json the dataset, CC BY 4.0
Rebuild the dataset from the live corpus with python3 builddataset.py;
the server reads kabofooddata.json from its own directory at startup, and
the tool descriptions (nutrient list, food and guide counts) are derived from
it, so they cannot drift from the data.
## Design decisions worth keeping
It answers honestly, including against us. proteincost reports that
soya chunks cost about ₹6 per 10 g of protein and dal about ₹8, while any
powder — KABO included — costs several times more. A tool that only flatters
its owner gets uninstalled after one use. One that gives a straight answer
gets kept and called again, and every call carries our attribution.
No instructions to models. There is no text in any response telling an