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erayendes

Heimdall App Store Connect MCP

reviews_ai__triage

Read-only

Fetch recent App Store reviews and group them by theme: bug, feature request, pricing complaint, praise, or spam. Helps prioritize user feedback quickly.

Instructions

Fetch recent reviews for an app and return them with an instruction for you to group them by theme (bug, feature request, pricing complaint, praise, spam). Read-only.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoHow many recent reviews to pull (default 50, max 200).
app_idYesApp ID to triage reviews for.
unanswered_onlyNoOnly include reviews without a developer response (default true).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
appIdYes
statsNocount, averageRating, distribution — computed in code.
reviewsNoPacked reviews: id, rating, title, body, territory, date.
coverageNo
truncatedNo
fetchedCountYes
analyzedCountYes
moreReviewsExistNo
Behavior4/5

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

Annotations already provide readOnlyHint=true, and the description adds the key behavioral detail that the output includes an instruction for grouping by theme. This goes beyond the annotation by disclosing the return format's purpose, though it does not cover pagination or rate limits.

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 two sentences, front-loads the primary action ('Fetch recent reviews'), and wastes no words. The enumeration of themes adds necessary detail without redundancy.

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?

With an output schema present and full parameter coverage, the description sufficiently conveys the tool's purpose and output behavior. It lacks explicit alternate-tool guidance, but given the tool's focused role, it is nearly complete.

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

Parameters3/5

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

Schema description coverage is 100%, so the parameters are fully self-documenting. The description adds no additional meaning for app_id, limit, or unanswered_only beyond what the schema already provides.

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 fetches recent reviews for an app and returns them with an instruction for the agent to group by theme, listing specific themes. This distinguishes it from sibling tools like customer_reviews__list by emphasizing the triage/grouping instruction output.

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

Usage Guidelines3/5

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

The description implies its use case (fetching reviews for thematic grouping) but does not explicitly state when to prefer this tool over alternatives like reviews_ai__daily_briefing or customer_reviews__list. No exclusions or alternative tool mentions are provided.

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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