Product Researcher MCP Server

An MCP server that gives Claude Code web search and product research capabilities. Never leave your terminal to research a product again.

What It Does

5 tools, one server:

Tool	What It Does
research_product	Full product brief — features, pricing, tech, API
compare_products	Side-by-side comparison of 2-5 products
lookup_pricing	Focused pricing/plans lookup
find_alternatives	Find competitors and alternatives
search_web	Raw web search for anything else

Quick Setup

1. Install dependencies

cd product-researcher-mcp
pip install -r requirements.txt

2. Pick a search provider and get an API key

Provider	Free Tier	Best For	Sign Up
Tavily	1,000/month	AI agents (returns summaries)	tavily.com
Brave	2,000/month	Privacy, good free tier	brave.com/search/api
Serper	2,500 total	Google-quality results	serper.dev

3. Set your environment variables

cp .env.example .env
# Edit .env — set SEARCH_PROVIDER and the matching API key

4. Add to Claude Code

Add this to your Claude Code MCP config (~/.claude/claude_desktop_config.json or project-level .mcp.json):

{
  "mcpServers": {
    "product-researcher": {
      "command": "python",
      "args": ["/full/path/to/product-researcher-mcp/server.py"],
      "env": {
        "SEARCH_PROVIDER": "tavily",
        "TAVILY_API_KEY": "tvly-your-key-here"
      }
    }
  }
}

Or if using Brave:

{
  "mcpServers": {
    "product-researcher": {
      "command": "python",
      "args": ["/full/path/to/product-researcher-mcp/server.py"],
      "env": {
        "SEARCH_PROVIDER": "brave",
        "BRAVE_API_KEY": "BSA-your-key-here"
      }
    }
  }
}

5. Test it

In Claude Code, just ask:

research Kling AI

compare Supabase vs Firebase vs PlanetScale

what's the pricing for Vercel

find alternatives to Midjourney for AI image generation

How It Works

You ask Claude Code
    → Claude calls research_product tool
        → MCP server builds smart search queries
            → Hits your search API (Tavily/Brave/Serper)
                → Deduplicates & formats results
                    → Returns structured markdown brief
                        → Claude synthesizes the answer for you

The server is search-API-agnostic. Swap providers any time by changing one env var.

File Structure

product-researcher-mcp/
├── server.py          # The MCP server (all tools + search adapters)
├── requirements.txt   # Python dependencies
├── .env.example       # Config template
└── README.md          # You're here

Extending

Want to add a new search provider? Add a function following the pattern:

async def _search_newprovider(query: str, max_results: int) -> List[Dict[str, Any]]:
    # Hit the API, return list of {"title": ..., "content": ..., "url": ...}
    pass

Then add it to the providers dict in the search() function. Done.

License

MIT

Author

Built by Quantaus