Read-only organic rank check across the app's target keywords, plus winnability-ranked opportunities computed from those measured positions. Positions are live-measured, never fabricated.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | App name, App Store URL, numeric id, or bundle id | |
| country | No | ISO country storefront, e.g. US | |
| bundleId | No | Exact bundle id (skips resolution) |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and openWorldHint, and the description reinforces read-only behavior. The added claim that positions are 'live-measured, never fabricated' provides valuable behavioral context about data provenance that annotations alone do not convey.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences with no filler, front-loads the read-only nature, and packs the key differentiators (live-measured, never fabricated) compactly. Every clause earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple tool shape, 100% schema coverage, and safety annotations, the description covers the primary output (rank positions and opportunities) and data reliability. It is slightly incomplete in that it does not explain behavior when no parameters are supplied, but overall it provides enough context for safe invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so parameters are already well documented in the input schema. The description adds no extra parameter-level meaning beyond 'target keywords' and 'measured positions', so the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states a specific verb and resource: 'read-only organic rank check' plus 'winnability-ranked opportunities' computed from measured positions. It also adds the distinguishing guarantee that positions are live-measured. However, it does not explicitly differentiate itself from related siblings like keyword_gaps or war_room.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies a use case — checking organic ranks and opportunities — but gives no explicit guidance on when to prefer this tool over siblings, no exclusions, and no named alternatives. An agent must infer when this is the right choice.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.