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by io.github.khan-ashifur
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analyze_product

Deeply validate ONE TikTok Shop product before committing money or content to it. Give it a product_id from product_scout (or any TikTok Shop product URL) and it reads the listing, samples reviews across the full depth rather than only the most recent page, pulls comparable competitors, and returns what buyers actually say. You get: provider-reported sold / review / rating / price evidence; buyer voice split into what buyers praise and what they complain about; unmet needs and purchase objections in buyers' own words; return and fulfilment signal; a Why Now read; competitor intelligence and where rivals are weak; risks separated into product risks, market risks and data-confidence risks; and a 'how to beat this' synthesis of concrete differentiation angles traced back to specific review evidence. Every section states whether it resolved - provider facts, HookLayer analysis and genuinely unavailable evidence are distinguishable, and thin evidence is reported as thin rather than filled in. There is no revenue or GMV output and sold counts are never multiplied by price. Use product_scout first to find candidates, then this to decide between them. Cost: 10 credits. Requires the shop_intelligence entitlement.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
marketNo2-letter ISO region code or 'GLOBAL'. Defaults to US. Should match the market the product was found in.
product_idNoTikTok Shop numeric product id, as returned by product_scout in opportunities[].product_id. Supply this or product_url.
product_urlNoAny TikTok Shop product URL (tiktok.com/shop/pdp/..., shop.tiktok.com/.../pdp/..., /view/product/...). Used when the user pastes a link instead of an id.
include_competitorsNoDefault true. Competitor discovery and the cross-product buyer-pain map are the differentiated part of this report; set false only when the caller explicitly wants the single-product read and fewer provider calls. The credit cost is the same either way.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
regionNo
successYes
economicsNoPrice and margin context. Contains no revenue or GMV figure.
fetched_atNo
product_idNo
provenanceNoEndpoints called, provider call count, staleness, and which fields the model wrote.
buyer_voiceNoReview sample summary including rating histogram and sampled count.
competitionNoPercentile placement against the peer set, when one was resolvable.
analyst_readNoModel-written operator summary grounded in the sections above. Falls back to a deterministic version if the model call fails.
market_proofNoDemand evidence section. Carries available / headline / data / evidence / risks / unknowns / confidence.
buyer_pain_mapNoThemes shared across this product and its competitors, with differentiation candidates. Rates are per-product, so a deeply sampled product does not outweigh a shallowly sampled rival.
analyst_verdictNoDeterministic verdict and confidence.
creator_contentNo
source_coverageNoWhich sections resolved and which did not.
validation_readNoExtended validation narrative. Null when include_competitors was false.
content_patternsNo
current_evidenceNoComposite score from currently observable evidence, with what was unavailable.
evidence_classesNoExplicit map so the host never has to guess provenance: fact[] came from the provider, analysis[] was derived by HookLayer, unavailable[] had insufficient evidence.
product_identityNotitle, shopName, category, productUrl, imageUrl.
credits_remainingNo
recommended_chainNoSuggested next calls, chosen from the evidence this analysis actually found. SUGGESTIONS ONLY - present them and let the user pick. Do not call them automatically; each one costs the user credits.
competitor_landscapeNoComparable products and where they are weak. available:false when competitor discovery did not resolve.
evidence_interpretationNoWhy Now plus risks split into product / market / data_confidence, each citing the observation that triggered it. Only claims what is observable now; never asserts a trend it has no history for.
buyer_voice_intelligenceNoOpportunity-grade review read: sampleComposition, love, painPoints, unmetNeeds, purchaseObjections, returnSignal, fulfilmentComplaints (kept separate from product faults), contradictions, and howToBeatThis. Null when reviews did not resolve - null means no evidence, not 'no complaints'.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.8/5.0
Behavior5/5

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

Annotations cover the safety profile (readOnlyHint=false, openWorldHint=true, idempotentHint=false, destructiveHint=false), and the description adds substantial context beyond them: 10-credit cost, entitlement requirement, no GMV/revenue output, sold counts never multiplied by price, evidence provenance being distinguishable, and thin evidence reported as thin rather than fabricated. This is unusually rich behavioral disclosure.

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?

Front-loaded correctly — purpose, input, and method come before the output enumeration. The 'You get:' list is long and partly duplicates the output schema, but each item is decision-relevant for a costly tool rather than filler.

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?

For a complex, credit-costing, entitlement-gated analysis tool, the description covers inputs, cost, gating, output shape, evidence-handling caveats, and sibling sequencing. Nothing needed to invoke it correctly is missing, even though the output enumeration is arguably redundant given the output schema.

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

Parameters4/5

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

Schema coverage is 100%, so parameters are already documented. The description still adds selection rationale for include_competitors ('the differentiated part of this report; set false only when... the credit cost is the same either way') and orients market/product_id to their source, going modestly beyond the schema.

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?

States a specific verb and resource ('Deeply validate ONE TikTok Shop product') and immediately positions itself against product_scout by framing the goal as 'before committing money or content to it.' An agent can distinguish it from all siblings, especially product_scout, without opening any schema.

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?

Explicit sequencing: 'Use product_scout first to find candidates, then this to decide between them.' It also names the precondition ('Requires the shop_intelligence entitlement') and the cost (10 credits), which are the practical gates an agent needs before calling.

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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