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audit_play_app

Read-only

Read-only GOOGLE PLAY listing audit: screenshot grade, the 30/80/4000 title/short/long-description budget, target-term coverage in the long description (Play's keyword surface), a keyword-stuffing guard, prioritized findings + summary, and capability locks for surfaces a public read can't see. Takes a Play package id (com.foo.bar) or a play.google.com URL — NOT a free-text name (Play has no public name search). Play has NO keyword field. Reads public Play data only; never edits or pushes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
brandNoApp brand name, so brand-burn in the short description is flagged
queryNoGoogle Play package id (com.foo.bar) or a play.google.com app URL
countryNoISO country storefront, e.g. US
targetsNoTarget search terms to measure long-description coverage for
packageNameNoExact Play package id (skips resolution)

Schema Changelog

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

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already carry readOnlyHint=true and openWorldHint=true, and the description does not contradict them. It adds genuine value beyond those hints: capability locks for owner-only surfaces, the platform fact that Play has no keyword field, the judging standards (30/80/4000), and the keyword-stuffing guard. It stops short of 5 because it does not disclose error behavior, rate limits, or how invalid package ids are handled.

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?

All core components are front-loaded in the first sentence, followed by input-format guardrails, a platform constraint, and a safety statement. Every clause earns its place; nothing repeats what the schema or annotations already state.

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?

This is a complex 5-parameter audit with no output schema, so the description bears the return-format burden. It gives a high-level output map (screenshot grade, prioritized findings + summary) and covers safety, input format, and platform constraints. The concrete response shape and error behavior remain unspecified, which would matter for an agent parsing the result.

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 the baseline is 3 with the schema carrying the load. The description adds one meaningful exclusion — 'NOT a free-text name (Play has no public name search)' — which goes beyond the schema's 'package id or URL' wording and prevents a common invocation error, and it ties brand/targets to their audit purposes.

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+resource: audit a Google Play listing, then enumerates exactly what the audit covers (screenshot grade, 30/80/4000 budgets, long-description keyword coverage, keyword-stuffing guard, prioritized findings + summary). The 'Read-only' framing plus 'capability locks for surfaces a public read can't see' separates it from sibling audit_play_app_owner, and the component list distinguishes it from narrower siblings like screenshot_coverage and keyword_gaps.

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

Usage Guidelines4/5

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

Gives explicit input constraints: accepts a Play package id (com.foo.bar) or a play.google.com URL, and explicitly excludes free-text names with a reason (Play has no public name search). 'Reads public Play data only' and 'never edits or pushes' imply when to choose this public-read tool versus an owner-level audit, though it never names sibling tools or states explicit when-to-use/when-not-to-use conditions.

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

A4/5.0
Disambiguation4/5

Tools are largely distinct by store, ownership, and analysis focus, with clear separation between App Store audits, Play audits, competitor diffs, keyword gaps, localization, rank checks, screenshots, and proof. A minor overlap exists between audit_app and preview_app, both of which read App Store listing data, though their outputs differ enough to avoid serious confusion.

Naming Consistency3/5

All names use snake_case and are readable, but the grammatical pattern is mixed: some are verb_noun (audit_app, propose_copy), some are noun phrases (keyword_gaps, localization_gaps, war_room), and a few are bare nouns (proof). There is no consistent verb-first or noun-first convention across the set.

Tool Count5/5

Twelve tools is well-scoped for an ASO intelligence server. Each tool addresses a meaningful part of the domain—auditing, competitor tracking, keyword/localization opportunities, rank checking, screenshot scoring, and proof—without feeling bloated or redundant.

Completeness5/5

The surface covers the core read-only ASO workflow: listing audits for both stores, owner-only Play audit, competitor monitoring, keyword and localization gaps, rank checks, screenshot coverage, draft copy proposals, and aggregate proof. The deliberate absence of write/publish tools is consistent with the server's stated human-approved loop, so no critical lifecycle gaps remain.