Product Research

Product Research

Command

/product-research "{PRODUCT_KEYWORD}" {SITE}

Goal

Evaluate a product opportunity and produce a decision-ready research report.

Credential Preflight

Before any data call, read {baseDir}/references/runtime-credential-preflight.md.

• If SellerSprite MCP is not configured, unavailable, or returns an authentication error, ask the user for the SellerSprite MCP API key before product research.
• If Gemini/GLM validation is part of the requested output and model keys are missing, ask for the missing keys before validation.
• Do not continue with mock MCP data or fabricated opportunity conclusions.
• Do not write provided keys into reports, raw files, dashboards, examples, or the open-source repository.

Required Reference

Read {baseDir}/references/sellersprite-mcp-api.md before execution. Normalize GB to SellerSprite MCP UK before tool calls.

Data Calls

• product_node: map the product direction to relevant Amazon category nodes.
• product_research: collect candidate products and opportunity-filtered samples.
• competitor_lookup: compare representative products by keyword, ASIN, brand, seller, node, month, and variation setting.
• market_research and market_research_statistics: quantify category market size and competition.
• market_price_distribution, market_rating_distribution, market_product_demand_trend: check pricing, maturity, and demand trend.
• market_product_concentration, market_brand_concentration, market_seller_concentration: quantify head concentration.
• keyword_miner, keyword_research, keyword_research_trends: validate keyword demand, CPC/PPC, purchase rate, supply-demand ratio, and trend.
• traffic_listing, traffic_source, traffic_extend: inspect traffic patterns for representative competitors when available.
• review: collect VOC from representative ASINs.
• google_trend: optional external demand validation.
• trademark_country_list, trademark_list, trademark_stats, trademark_detail: use only for naming/IP/trademark risk checks.

If MCP cannot answer a required question, mark the gap and then use a crawl/browser fallback only for that missing evidence.

Workflow

1. Clarify product keyword and site.
2. Normalize site code for SellerSprite MCP.
3. Collect representative products using product_research and competitor_lookup.
4. Collect keyword, category, market distribution, and traffic context from MCP.
5. Save raw or compact MCP responses before reasoning.
6. Label product attributes: price, rating, reviews, feature, material, scenario.
7. Compare opportunity patterns with evidence references.
8. Analyze VOC and barriers using review/category/trademark evidence or labeled assumptions.
9. Output decision: enter, observe, or reject.

Research Dimensions

Do not invent supply-chain, patent, compatibility, seasonality, inventory, or certification risks. Use MCP data first, then existing user files, then explicitly labeled assumptions or crawl evidence.

Output

Save under:

product-research-reports/{PRODUCT_KEYWORD}_{SITE}_{YYYYMMDD}/report.md

Do not publish raw product exports or private cost assumptions.