ProductLens MCP 🔍

A Product Intelligence MCP server — exposes product comparison and analysis tools that any MCP-compatible AI assistant (Claude Desktop, ChatGPT, Cursor, VS Code) can discover and use.

Built with FastMCP. Demo dataset: Indian compact SUVs.

Demo

▶️ Watch the 90-second demo — Claude Desktop autonomously chaining compare_products → swot_analysis → recommend_product to answer: "Compare the Brezza and Nexon, give me a SWOT of the winner, and tell me what to buy under ₹9 lakh if safety matters most."

Related MCP server: BopMarket MCP Server

Why MCP?

Instead of building a chatbot locked into one AI platform, ProductLens is a service. Build the logic once; every MCP client can call it:

Claude ─┐
ChatGPT ─┼──► ProductLens MCP ──► data / logic
Cursor ─┘

The server describes its own tools (names, arguments, docs), so AI clients discover them automatically — no custom integration per assistant.

Tools

Roadmap: summarize_reviews, generate_prd, a second product category (phones) to prove the server is category-agnostic.

Quick start

git clone https://github.com/<you>/ProductLens-MCP.git
cd ProductLens-MCP
pip install -r requirements.txt
python server.py          # runs over stdio for MCP clients
python test_client.py     # smoke-test via a real MCP client

Requires Python 3.10+ (FastMCP does not support older versions).

Connect to Claude Desktop

Add to claude_desktop_config.json (Settings → Developer → Edit Config):

{
  "mcpServers": {
    "productlens": {
      "command": "/full/path/to/your/python",
      "args": ["/full/path/to/ProductLens-MCP/server.py"]
    }
  }
}

Note: use the full path to the Python that has fastmcp installed (find it with which python). Claude Desktop launches the server itself, so a bare "python" may resolve to a different interpreter — especially with conda environments.

Restart Claude Desktop fully (Cmd+Q), then ask: "Compare Brezza and Nexon."

Project structure

ProductLens-MCP/
├── server.py          # MCP layer — thin, just registers tools
├── tools/
│   ├── compare.py     # head-to-head comparison logic
│   ├── swot.py        # SWOT vs segment average
│   └── recommend.py   # budget + priority recommendations
├── data/
│   └── cars.csv       # demo dataset (swap for any product CSV)
├── test_client.py     # end-to-end MCP protocol test
├── demo.mp4           # Claude Desktop demo recording
└── requirements.txt

Design principle: logic and protocol are separated. Everything in tools/ is plain Python that knows nothing about MCP — it could power a web app or CLI unchanged. server.py is just the MCP "waiter."

Sample output

compare_products("Brezza", "Nexon") returns structured JSON — per-metric winners, each product's advantages, and an overall edge — which the AI client turns into a natural-language answer. Errors are AI-friendly too: an unknown product returns the list of available products, so the assistant can recover in conversation instead of dead-ending.

Challenges & learnings

• numpy vs JSON: pandas returns numpy int64/float64, which fail MCP's structured-output validation. Fixed by converting to native Python types before returning.

• Docstrings are the interface: the AI selects tools purely from their descriptions, so writing them as "what + when to use" directly improved tool selection (e.g., disambiguating SWOT-of-one vs compare-two).

• stdio means the client launches you: Claude Desktop starts the server itself as a subprocess, so the config must point to the conda environment's Python — a bare python resolved to an old 3.8 interpreter without fastmcp.