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.

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.

Related MCP server: MCP Product Search Server

Tools

More coming: swot_analysis, summarize_reviews, generate_prd, prioritize_features.

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

Connect to Claude Desktop

Add to claude_desktop_config.json:

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

Restart Claude Desktop, then ask: "Compare Brezza and Nexon."

Project structure

ProductLens-MCP/
├── server.py          # MCP layer — thin, just registers tools
├── tools/
│   └── compare.py     # Pure logic — knows nothing about MCP
├── data/
│   └── cars.csv       # Demo dataset (swap for any product CSV)
├── test_client.py     # End-to-end MCP protocol test
└── requirements.txt

Design principle: logic and protocol are separated. tools/ 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.