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Webotee Amazon Product Research

evaluate_asin_sourcing

Read-only

Assess an ASIN's sourcing viability using a 0-100 composite score, demand estimate, and red-flag list to guide your FBA purchase decision.

Instructions

Evaluate a single ASIN for FBA sourcing. Returns composite sourcing score (0-100), 5-dimension breakdown (velocity, gating, friction, margin, brand_posture), estimated 30-day demand (units_30d_final + est_revenue_30d) with its source/badge_band/confidence, a data_coverage flag (full vs velocity_only — so a null demand reads as a coverage gap, not zero sales), star rating + review count (with a rating_coverage flag), brand-level FBA/Amazon dominance, and a red-flag list. Also returns the product brand, title, and price (or price range) plus the ASIN's fulfillment (FBA/FBM/AMZ + amz/fba pct). Use when the user asks 'should I buy this?', 'how fast does this sell?', or shares an ASIN and wants a sourcing recommendation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
asinYesAmazon ASIN, 10-character alphanumeric.
marketplace_idNo1 = Amazon UK, 2 = Amazon US (default)
qtyNoOptional purchase quantity for ROI sizing.
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations indicate readOnlyHint=true, and description consistently details all return fields, including coverage flags and explanation of null demand, adding value beyond annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured, listing outputs then usage guidance. It is somewhat lengthy but every sentence adds value; could be slightly more concise.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given complexity (no output schema), the description thoroughly explains all return values including edge cases like data_coverage flag. Context including sibling tools is comprehensive.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema covers 100% of parameters with descriptions. The tool description does not add significant extra meaning beyond what the schema already provides, so baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Evaluate a single ASIN for FBA sourcing' and lists specific outputs, distinguishing it from sibling tools like find_sourcing_opportunities or asin_profit_calc.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly says 'Use when the user asks 'should I buy this?', 'how fast does this sell?', or shares an ASIN and wants a sourcing recommendation.' This provides clear context for when to invoke.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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