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Glama

Apiguru Amazon Data

by app.apiguru

Report a bug or request a feature (free)

send_feedback

Report a bug, ask for a field, or say what would make this API more useful. Free: never billed, no API key needed.

    Prefer GitHub if you have an account -- an issue at
    https://github.com/apiguru-app/agent-kit/issues gets a reply on the
    thread, this wall does not. Use this tool when you have no GitHub
    account or nothing to attach one to.

    message:  what happened or what you want. Be specific: the tool, the
              parameters, the field, what you expected, what you got.
    category: bug | wish | praise | question | other
    endpoint: which tool or path it is about, e.g. "search".
    agent:    what you are, e.g. "acme-pricing-bot/2.1". Optional.
    contact:  a GitHub handle or email if you want a reply. Shown
              publicly on the wall. Optional.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
agentNo
contactNo
messageYes
categoryNoother
endpointNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.7/5.0
Behavior4/5

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

Annotations carry the base safety profile (not read-only, not idempotent, not destructive, open-ended), and the description adds meaningful context beyond them: 'Free: never billed, no API key needed' discloses cost and auth behavior, while 'this wall does not [get a reply]' and 'Shown publicly on the wall' disclose visibility and response expectations. No contradiction 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.

Conciseness5/5

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

The description is well-structured: purpose statement, cost/auth note, routing guidance, then a cleanly formatted parameter block. Every sentence earns its place, and the most decision-relevant facts (what it does, that it's free, when to use it) are front-loaded.

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

Completeness4/5

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

For a tool with 5 parameters, an output schema, and clear sibling differentiation, the description covers everything an agent needs to invoke it correctly: purpose, cost, auth, alternatives, all parameter semantics, and privacy implications of contact. The only minor gap is the mechanics of what happens after submission, but the output schema covers return values and the wall behavior is referenced.

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 description coverage is 0%, so the description fully compensates by documenting all five parameters with concrete guidance. It explains message specificity ('the tool, the parameters, the field, what you expected, what you got'), enumerates category values (bug | wish | praise | question | other), and gives examples for endpoint and agent.

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 opening line 'Report a bug, ask for a field, or say what would make this API more useful' states a concrete action and resource (submitting feedback about the API). It is unmistakably distinct from the commerce/product sibling tools like best_sellers and product_reviews, so an agent cannot confuse it with them.

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 routing guidance is provided: 'Prefer GitHub if you have an account' with a named alternative URL, plus the exact condition for using this tool instead: 'Use this tool when you have no GitHub account or nothing to attach one to.' This is textbook when/when-not usage guidance.

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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TDQS

A4.1/5.0
Disambiguation4/5

Most tools target clearly distinct resources—product details, reviews, offers, sellers, deals—and the batch/meta tools are obvious. A couple of adjacent tools could be confused (product_details vs offers_stock, or search by seller vs seller_products), but the descriptions are detailed enough to prevent serious misselection.

Naming Consistency3/5

Names are all snake_case and use readable resource prefixes (product_*, seller_*), but the verb/noun pattern is inconsistent: bare plural nouns (best_sellers, deals), single verbs (search), and compound nouns (offers_stock, product_details_batch) are mixed. It is readable but not a uniform convention.

Tool Count5/5

Twelve tools is a well-scoped size for an Amazon data API. Each tool serves a distinct data-access or meta purpose with no obvious redundancy or bloat.

Completeness4/5

The toolkit covers the core Amazon data surface: product lookup (single/batch), discovery (search, best sellers, deals), offers/stock, reviews, and seller information. Minor gaps exist—no category-tree endpoint and review pagination is not explicit—but primary agent workflows are covered.

Resources