ProductPilot MCP

An MCP server that helps digital product creators go from "what should I build?" to a zipped, listing-ready product — end to end.

Built for creators shipping ebooks, prompt packs, wallpaper packs, Notion templates, checklists, printable planners, cheat sheets, resume packs, and study guides to Gumroad/Etsy.

What it does

Ask Claude (or any MCP client connected to this server) things like:

• "What digital product should I build today?" → find_trending_niches, score_opportunity
• "Is this niche worth pursuing?" → research_market, analyze_competition, analyze_reviews
• "Build me a complete product for this niche" → build_product_bundle (the one-shot pipeline)
• "Write the listing copy" → generate_listing, generate_keywords, generate_tags
• "What should this cost?" → generate_pricing_strategy
• "Prep this for Gumroad/Etsy" → prepare_gumroad_listing, prepare_etsy_listing
• "What sold well, and what should I build next?" → track_product_performance, recommend_next_product

26 tools total — see mcp_server/server.py for the full list, or docs/ARCHITECTURE.md for how they fit together.

Quick start

python3 -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
cp .env.example .env
python -m mcp_server.server

Runs entirely on deterministic mock market data out of the box — no API keys required to try it. Full setup/deployment instructions: docs/SETUP.md, docs/DEPLOYMENT.md.

Using this on claude.ai (web)

Claude.ai's web app only connects to remote MCP servers (a public HTTPS URL) — not local processes. This server supports that via PRODUCTPILOT_TRANSPORT=streamable-http. Full walkthrough (deploy to Render

• add as a claude.ai custom connector): docs/CLAUDE_AI_CONNECTOR.md.

Status

Functionally complete: all 26 tools registered and runtime-tested end to end (idea → outline → generation → SEO → zipped bundle → analytics tracking), 18 passing unit tests, Docker support.

Known environment gotcha (already fixed here, documented for reference): the mcp PyPI package's 2.0.0 release renamed FastMCP to MCPServer and dropped mcp.server.fastmcp entirely — requirements.txt pins mcp[cli]<2.0.0 to stay on the FastMCP-based API this server is built against. See docs/ARCHITECTURE.md for the full explanation, including a second subtle bug it's worth knowing about (from __future__ import annotations breaking FastMCP's tool introspection).

Project layout

mcp_server/    # all source (agents, market, research, scoring, generation,
               # seo, publishing, analytics, storage, config)
tests/         # pytest suite
docs/          # architecture, setup, deployment, roadmap

See docs/ARCHITECTURE.md for the full breakdown, docs/ROADMAP.md for what's next.