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ncosic

logimu-shopping-mcp

product

Read-only

Retrieve a complete product dossier including price history, stock history, seller history, and current snapshot for a specific ASIN or Walmart item ID.

Instructions

Full dossier for ONE known product: its current snapshot plus its observed history. USE WHEN the user has a specific ASIN, Walmart item ID, product link, or a product_id returned by shopping or search, and asks about price history, historical prices, price changes, 30-day history, stock history, seller history, buy-box history, historical analysis, 'analyse this product', 'is this a good buy', 'has the price moved/dropped', 'who is selling this', 'is it in stock'. This is the ONLY tool that returns history: shopping and search return current values, so any historical question about a product they listed comes here. DON'T USE to discover products from a keyword (use shopping) or to pull a filtered list (use search). RETURNS current price, BSR, rating, review count, stock, buy-box seller and seller count, plus an observed_at freshness stamp, full price_history and stock_history back to first observation (keyed; the free lane carries the 30-day views), change events tagged with the buy-box seller at each change, the current all-seller offer table with 30-day buy-box days, the bought-past-month badge (measured aggregate buyer behavior, not an estimate), and brand stats. For the ~17% of the catalog with no overall rank (media, books, niche items), bsr_leaf and bsr_leaf_category carry the best category rank instead. Every response carries a data_source field naming the marketplace the numbers were observed on (e.g. 'amazon US marketplace — observed listings') — attribute prices to that source when presenting them; they are marketplace listings, not manufacturer or site-wide prices. MARKETPLACES us, uk, de, ca, au, walmart. Walmart takes a numeric item ID and returns the intelligence blocks only (no live scrape). COST free lane 1 of 30 daily queries, cache only, and returns the snapshot + 30-day views (the full history streams, bsr_history, offer_history and live scrapes need a free API key — the response's locked block lists exactly what a key unlocks). Keyed: 0.5 credits from cache, 1 for a live scrape, +0.5 for the intelligence blocks, +0.5 each for bsr_history and offer_history. Misses and partial scrapes are never billed; a miss may return a hint (found on another marketplace, or retry with mode=live).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
asinYes10-character Amazon ASIN, or a numeric Walmart item ID when country=walmart.
modeNocache = stored observation only; live = force an on-demand scrape (Amazon only, takes a few seconds); auto = serve cache when fresher than max_age_days, otherwise scrape. The no-signup free lane is cache-only: mode=live returns an error asking for a free API key (do not offer a live scrape to a keyless caller); with a key, live/auto scrape normally.auto
countryNoMarketplace to look the product up in. Amazon: us, uk, de, ca, au. walmart = Walmart US (United States only). Pick the marketplace matching the user's country or locale when known (a German user -> de, a Canadian user -> ca); default us.us
bsr_historyNoAttach the full per-category BSR rank history (era-tagged daily points back to Oct 2023 for US; legacy top-100 segments are flagged censored). Amazon marketplaces only, API key required (free key works). +0.5 credits when data is returned.
max_age_daysNoHow old a cached observation may be before mode=auto triggers a live scrape.
offer_historyNoAttach the buy-box owner timeline and per-seller daily price series (US buy-box depth back to Dec 2024). Amazon marketplaces only, API key required (free key works). +0.5 credits when data is returned.
Behavior5/5

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

Annotations provide readOnlyHint and openWorldHint; the description adds extensive behavioral detail: data_source field, special case for BSR on ~17% of catalog, cost structure, miss handling, marketplace specifics (Walmart returns only intelligence blocks), and free lane limitations. No contradictions with 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 lengthy but well-structured: front-loaded with purpose, then usage, return details, special cases, cost. Every sentence adds value, though some cost details could be condensed. It earns a 4 for being comprehensive without being rambling.

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?

Despite no output schema, the description thoroughly explains return data (current price, BSR, rating, etc., plus history fields), special cases, supported marketplaces, cost model, and error handling. It covers all likely agent needs for invoking the tool correctly.

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 description coverage is 100% (all parameters have descriptions in schema), baseline 3. The description adds extra context: asin can be Walmart item ID, mode=live requires key, country mappings to marketplaces. This adds value beyond 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?

The description states 'Full dossier for ONE known product: its current snapshot plus its observed history,' which is a specific verb and resource. It explicitly distinguishes from sibling tools 'shopping' and 'search' by noting they return current values while this returns history.

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?

The description provides explicit when-to-use scenarios (e.g., 'user has a specific ASIN... and asks about price history') and lists what it is NOT for ('DON'T USE to discover products from a keyword (use shopping) or to pull a filtered list (use search)'). It names alternatives clearly.

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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