Read-only, anonymized aggregate proof across all tracked apps (real rank-win numbers — no app names, no user data). This is the 'prove the rank moved' surface that closes the prepare → approve → push → prove loop. Works without a key.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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=true and openWorldHint=true, so the safety profile is covered. The description adds meaningful behavioral context: the result is anonymized, aggregated, contains real rank-win numbers, excludes app names and user data, and requires no API key. This goes beyond the annotations and gives an agent confidence about privacy and access expectations.
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?
Two sentences with no filler: the first sentence front-loads the read-only, anonymized, aggregate nature and the second provides the strategic workflow context. Every clause earns its place, and the key distinguishing traits come first.
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?
For a tool with no parameters, no output schema, and annotations covering its read-only/open-world nature, the description is fully complete. It explains what data is returned, what is NOT returned, the authentication requirement, and the workflow purpose. An agent has everything needed to decide whether and how to call it.
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?
The tool has zero parameters and the schema description coverage is 100%, so there are no parameter semantics to clarify. The baseline for zero-parameter tools is 4, and the description adds useful context about what the returned aggregate represents, even though no parameter details are needed.
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 identifies a unique resource: read-only, anonymized aggregate proof across all tracked apps, with real rank-win numbers and no app names or user data. It positions this as the 'prove the rank moved' surface, which makes its purpose unmistakable even without an explicit verb like 'get' or 'list'. It does not explicitly name sibling alternatives, so it stops short of full differentiation.
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 when to use it: when you need to prove that a rank moved, closing the prepare → approve → push → prove loop. It also states it works without a key, which is a useful prerequisite clarification. However, it does not state when not to use it or point to alternatives among the sibling tools.
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.