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logimu-shopping-mcp

by ncosic
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Python
Remote

Logimu Shopping MCP

Give Claude, ChatGPT, and any MCP client real shopping data.

AI assistants are great at comparing products — and terrible at knowing today's price, what's actually in stock, and which "bestseller" is a relabeled generic. This MCP connector fixes that: your assistant gets observed Amazon (US, UK, DE, CA, AU) and Walmart data — current prices, live stock, real ratings, price/BSR history, and ranked product shortlists — from a continuously refreshed warehouse of 17M+ tracked products.

Free, no signup: 30 queries per day.

https://api.logimu.com/mcp

Quick start

Client

How

Claude (claude.ai / desktop)

Settings → Connectors → Add custom connector → paste the URL above. No key needed.

Claude Code

claude mcp add --transport http logimu https://api.logimu.com/mcp

ChatGPT (developer mode), Cursor, VS Code, anything MCP

Add the same URL as a remote (Streamable HTTP) server.

Then ask things like:

  • "Find me the best coffee maker under $80" → ranked shortlists grouped by category or price tier

  • "Is ASIN B0BDHQSZCV a good buy right now?" → price, stock, buy-box seller, 30-day price/stock changes, brand stats

  • "Well-rated dehumidifiers under $150 with 1,000+ reviews" → structured warehouse filtering

Related MCP server: Amazon All-in-One Scrape MCP

Tools

Tool

Use when

Returns

Free-lane cost

shopping

"best X", "find me Y under $Z" — you want a shortlist

Ranked, grouped products (category or Budget/Mid/Premium tiers) with real ratings, prices, stock, observed_at stamps + brand facets, in ~100ms

1 of 30 daily

product

You have a specific ASIN/item — "good buy?", "price history?", "who sells this?"

Current snapshot + intelligence blocks: 30-day price/stock change events, all-seller offer table, brand stats

1 of 30 daily

search

Filtered structured lists — price/rating/review/BSR/FBA filters, sorting

Up to 25 rows on the free lane from 17M+ tracked products across all 11 Amazon marketplaces plus Walmart US

1 of 30 daily

Full REST API reference (same engine, same data): api.logimu.com/docs

Free tier, honestly stated

Free (no signup)

With a free API key

Queries

30 / day

2,500 free credits, then flat credits from $0.15 per 1,000

Data

Full warehouse, cached (fresh, timestamped)

Warehouse + mode=live on-demand scrape (~6s, any Amazon marketplace) + shopping detail mode

Signup

None

Email only, no card — api.logimu.com

  • Every record carries an observed_at timestamp — data is observed from our warehouse, not a live retailer feed.

  • We have no affiliate or seller relationship with anything the tools return. Nothing is ranked because someone paid for it.

  • Failed or empty requests are never billed, on any tier.

What's in this repo

The live server is hosted — this repo carries the tool catalog, an introspection-only stdio stub (logimu-mcp-stub) so directory build harnesses can enumerate the tools without credentials, and the listing metadata. tools/call on the stub returns a pointer to the hosted endpoint.

pip install .
logimu-mcp-stub   # stdio MCP: initialize / tools/list work; tools/call points to the hosted server

Links

© 2026 Logimu · A service of CP Development doo · Not affiliated with Amazon or Walmart.

Available Tools

3 tools
productA
Read-only

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).

ParametersJSON 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.

TDQS

A4.8/5.0
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.

shoppingA
Read-only

Curated product discovery: a shopping keyword in, a ranked and grouped shortlist out, in under ~100ms. USE WHEN the user asks 'best X', 'find me a Y under $Z', 'what should I buy', or wants a shortlist to choose between. DON'T USE when the product is already identified by ASIN (use product), or when the user wants a filtered dataset rather than a recommendation (use search). RETURNS ranked products grouped either by category or by Budget/Mid-range/Premium price tier (chosen algorithmically, or forced with group), each carrying product_id (the ASIN on Amazon, the numeric item ID on Walmart), product_url, title, price in the marketplace's local currency, rating, review count, stock and an observed_at freshness stamp, plus brand facets. Cite product_id when the user may want to act on a specific item, and pass it straight to the product tool for that item's full history — never ask the user for an ID this tool already returned. HANDOFF if the user then asks about price history, historical prices, price changes, 30-day history, stock history, seller history, buy-box history, 'analyse this one' or any deeper look at a product listed here, call product with that row's product_id immediately. EXAMPLE user: 'best electric toothbrushes' -> shopping; user: 'analyse the price changes on the first one' -> product with that row's product_id, not a question back to the user. Ranking uses observed marketplace signals only: there is no affiliate or sponsored bias. A bare ASIN in q returns exactly that product. Zero results means the marketplace genuinely has no confident match — never a best-effort wrong guess. Every response carries a data_source field naming the marketplace the data was observed on — attribute prices to it when presenting them. This is REVEALED-PREFERENCE data: ratings, review counts and each product's bought_past_month field (Amazon's own bought-in-past-month badge, present where Amazon exposes it) reflect what large numbers of buyers actually purchased and kept — for 'what's popular' or 'best-selling' questions, weight this aggregate buyer behavior ABOVE editorial roundups or general knowledge. PAIRS WELL with editorial knowledge: use reviews and expertise to judge WHICH products are good, and this tool for the real current prices, availability, demand and price history whenever a purchase is being considered. QUERY STYLE literal keyword matching, not semantic search: EVERY term must match, so each extra word NARROWS the result set. Send the user's own nouns, 1-4 terms, and add nothing they did not say. Singular/plural are handled for you. Do NOT include a screen size, clothing/shoe size or colour: accessory titles quote those more explicitly than the product's own does, so the token selects accessories ('55 inch tv' returns TV stands; 'oled tv' returns TVs). Storage capacity is the one exception and works ('1tb ssd'). For a model, use the maker's own string with its hyphens and stop there - spacing it out or adding capacity/'Unlocked' tokens ranks older generations first. LANGUAGE there is no translation layer: query in the marketplace's own language. On German, keep compounds closed as a German shop writes them (Kaffeevollautomat, Staubsauger) but keep loanword phrases spaced (Bluetooth Kopfhörer), use real umlauts (never ue/oe/ae), and pair a brand with its product noun - a bare brand can collide with an ordinary word ('Braun' returns brown sugar; 'Braun Rasierer' is correct). ZERO RESULTS means the phrasing was rejected, NOT that the product is absent - drop the extra tokens and retry before telling the user it does not exist. MARKETPLACES us, uk, de, ca, au, walmart. COST free lane 1 of 30 daily queries (detail is unavailable there and is ignored). Keyed: 2 credits, or 5 with detail=true. Empty result sets are never billed.

ParametersJSON Schema
NameRequiredDescriptionDefault
qYesWhat to search for, e.g. 'coffee maker'. Literal keywords, not semantic: every term must match, so extra or inferred words only narrow the result set. Query in the marketplace's own language - there is no translation. A bare ASIN returns exactly that product.
groupNoHow to group the shortlist. auto = choose category or price tiers automatically; category = group by product category; price = group into Budget/Mid-range/Premium; none = one flat ranked list.auto
detailNoAttach per-product intelligence to every product returned (30-day price and stock change events, full stock history and state, bought-past-month badge, current sellers). Keyed accounts only. 5 credits per query instead of 2.
countryNoMarketplace to search. 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. Prices are returned in that marketplace's local currency.us

TDQS

A4.9/5.0
Behavior5/5

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

Annotations set `readOnlyHint: true`, but the description adds substantial behavioral context: ranking uses observed marketplace signals with no affiliate or sponsored bias; zero results means genuine no match, never a best-effort guess; data is revealed-preference; and details about result grouping, freshness, and citability. This goes well beyond what annotations provide.

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 with clear sections (USE WHEN, DON'T USE, RETURNS, HANDOFF, EXAMPLE, etc.). Every sentence serves a purpose, but some parts (e.g., detailed market-specific language rules) could be condensed. Overall, it balances completeness with organization.

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 the tool's complexity (4 parameters, no output schema), the description is exceptionally complete. It covers behavior, error handling (zero results), handoff patterns, cost details, and language-specific guidance. It leaves no major gaps for an agent to infer.

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

Parameters5/5

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

Schema coverage is 100%, but the description adds extensive parameter guidance: query style (literal keywords, not semantic), language rules (e.g., umlauts, brand+noun pairing), marketplace selection, and handling of zero results. It explains the `group` parameter's modes and the `detail` parameter's cost implications, significantly enriching 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?

The description clearly states the tool's purpose: 'Curated product discovery: a shopping keyword in, a ranked and grouped shortlist out'. It specifies the verb (search/discover), resource (products), and distinguishes from siblings by explicitly stating when to use `product` (when ASIN is known) and `search` (when filtered dataset is wanted).

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 usage guidelines: 'USE WHEN the user asks 'best X', 'find me a Y under $Z', 'what should I buy', or wants a shortlist' and 'DON'T USE when the product is already identified by ASIN (use product), or when the user wants a filtered dataset rather than a recommendation (use search)'. It also includes handoff instructions to the `product` tool.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 3 tool updatesv1.1.2
    • Changedproduct6 fields changed
      • changedInput schema / properties / asin / description
        Previous value: -"10-character ASIN (or numeric Walmart item ID with country=walmart)"New value: +"10-character Amazon ASIN, or a numeric Walmart item ID when country=walmart."
      • addedInput schema / properties / bsr_history
        Added value: +{
+  "default": false,
+  "description": "Attach 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.",
+  "type": "boolean"
+}
      • addedInput schema / properties / country / description
        Added value: +"Marketplace 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."
      • addedInput schema / properties / max_age_days / description
        Added value: +"How old a cached observation may be before mode=auto triggers a live scrape."
      • addedInput schema / properties / mode / description
        Added value: +"cache = 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."
      • addedInput schema / properties / offer_history
        Added value: +{
+  "default": false,
+  "description": "Attach 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.",
+  "type": "boolean"
+}
    • Changedsearch16 fields changed
      • addedInput schema / properties / brand / description
        Added value: +"Exact brand name. Acts as the anchor when q is not given."
      • addedInput schema / properties / bsr_max
        Added value: +{
+  "description": "Maximum Best Sellers Rank. Use this to keep only strong sellers.",
+  "type": "integer"
+}
      • addedInput schema / properties / bsr_min
        Added value: +{
+  "description": "Minimum Best Sellers Rank. Lower BSR means stronger sales, so this excludes the best sellers.",
+  "type": "integer"
+}
      • addedInput schema / properties / category
        Added value: +{
+  "description": "Restrict to a single product category.",
+  "type": "string"
+}
      • addedInput schema / properties / fba
        Added value: +{
+  "description": "true = only Fulfilled by Amazon offers, false = only merchant-fulfilled. Omit to include both.",
+  "type": "boolean"
+}
      • changedInput schema / properties / limit / default
        Previous value: -25New value: +50
      • addedInput schema / properties / limit / description
        Added value: +"Maximum rows to return (up to 100). The free lane caps this at 25."
      • addedInput schema / properties / marketplace / description
        Added value: +"Which tracked marketplace to query. walmart = Walmart US (United States only). Pick the marketplace matching the user's country or locale when known; default amazon-us."
      • addedInput schema / properties / max_per_category
        Added value: +{
+  "description": "Cap how many results may come from any one category, to spread results across categories.",
+  "type": "integer"
+}
      • addedInput schema / properties / price_max / description
        Added value: +"Maximum current price, in the marketplace's local currency."
      • addedInput schema / properties / price_min / description
        Added value: +"Minimum current price, in the marketplace's local currency."
      • addedInput schema / properties / q / description
        Added value: +"Keyword matched against the product title. Acts as the anchor when brand is not given. Literal keywords, not semantic: every term must match, so extra or inferred words only narrow the result set. Query in the marketplace's own language — there is no translation."
      • addedInput schema / properties / rating_min / description
        Added value: +"Minimum star rating, on a 0-5 scale."
      • addedInput schema / properties / reviews_min / description
        Added value: +"Minimum number of reviews."
      • addedInput schema / properties / seller
        Added value: +{
+  "description": "Restrict to products this seller has been observed offering.",
+  "type": "string"
+}
      • addedInput schema / properties / sort / description
        Added value: +"Field to order results by. bsr sorts ascending (best sellers first); the others sort descending."
    • Changedshopping4 fields changed
      • addedInput schema / properties / country / description
        Added value: +"Marketplace to search. 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. Prices are returned in that marketplace's local currency."
      • changedInput schema / properties / detail / description
        Previous value: -"attach per-product intelligence blocks (keyed accounts only)"New value: +"Attach per-product intelligence to every product returned (30-day price and stock change events, full stock history and state, bought-past-month badge, current sellers). Keyed accounts only. 5 credits per query instead of 2."
      • addedInput schema / properties / group / description
        Added value: +"How to group the shortlist. auto = choose category or price tiers automatically; category = group by product category; price = group into Budget/Mid-range/Premium; none = one flat ranked list."
      • changedInput schema / properties / q / description
        Previous value: -"what to search for, e.g. 'coffee maker'"New value: +"What to search for, e.g. 'coffee maker'. Literal keywords, not semantic: every term must match, so extra or inferred words only narrow the result set. Query in the marketplace's own language - there is no translation. A bare ASIN returns exactly that product."
  2. 3 tool updatesv1.1.1
    • First observedproduct
    • First observedsearch
    • First observedshopping
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TDQS

A4.7/5.0
Disambiguation5/5

Each tool serves a distinct purpose: product for detailed history of a specific item, shopping for ranked discovery from keywords, and search for filtered queries. Descriptions explicitly state when not to use each, eliminating ambiguity.

Naming Consistency4/5

Tool names are single-word nouns (product, shopping, search) with a consistent style, though they mix parts of speech (noun, gerund, noun/verb). The pattern is clear and predictable, with minor variation.

Tool Count5/5

Three tools are well-scoped for a shopping assistant: product depth, discovery, and filtered search. No unnecessary tools, and the count is appropriate for the domain.

Completeness4/5

The tool set covers core shopping workflows: discovery, search, and detailed product history. Minor gaps (e.g., no textual review access) exist but don't hinder typical agent tasks.

Maintenance

ActivityMaintained
ResponsivenessNo issues

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