Decision Intelligence

App Release Regression Intelligence

app-release-regression-intelligence

Compare supplied app reviews around a release and return regression and unsent action findings. — $0.08/call, x402 (USDC on base).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`rows`	No	Canonical review rows. Use this or datasetId, never both. Runtime validation enforces the mutual exclusion.
`appId`	Yes	Canonical App Store ID, package name, or other buyer-owned application identifier.	demo.app
`appName`	No	Optional display name for the application.	Demo App
`options`	No	Bounded deterministic analysis and optional explanation settings.
`watchId`	Yes	Stable application watch identity used for baseline comparison.	spotify-us-ios
`platform`	Yes	Review source platform.	apple
`datasetId`	No	One buyer-owned Dataset selected with read-only permission. Use this or rows, never both. Runtime validation enforces the mutual exclusion.
`releaseAt`	Yes	UTC release timestamp. Pre and post windows use this instant as their boundary.	2026-08-08T00:00:00Z
`requestId`	Yes	Use auto for a Task-safe trusted Actor run ID, or provide an explicit idempotency key.	auto
`schemaVersion`	Yes	Contract version for this Actor input.	1.0
`releaseVersion`	No	Optional exact version used for cautious causality evidence.	1.0.0
`openrouterApiKey`	No	Optional secret used only when options.explanation is byok.
`maxTotalChargeUsd`	No	Optional buyer cap checked before the paid result event.

TDQS

A4.1/5.0

Behavior4/5

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

Annotations already indicate non-read-only and open-world behavior, and the description adds the notable cost/payment detail ('$0.08/call, x402 (USDC on base)') and the 'unsent action findings' outcome. It does not fully explain side effects, but the annotations lower the burden and this adds useful context beyond them.

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 a single sentence, front-loaded with purpose, followed by a concise pricing note. Every element serves a purpose, and there is no redundancy or filler.

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

Completeness3/5

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

While the schema is rich, the description omits important operational context such as the mutual exclusivity of rows/datasetId, the deterministic analysis options, and the absence of an output schema. For a complex 13-parameter tool, a bit more prose would help, but the schema mitigates the gap.

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?

The input schema covers 100% of parameters with descriptions, so the schema does the heavy lifting. The tool description itself adds no parameter-level meaning beyond what is already in the schema, warranting the baseline of 3.

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 compares supplied app reviews around a release and returns regression and unsent action findings, using a specific verb and resource. This is distinct from sibling tools (competitor-ad-change-brief, pricing_info, us-map-retail-evidence) which address unrelated domains.

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?

The description provides clear context that the tool is for analyzing app reviews around a release, but it doesn't explicitly say when not to use it or name alternatives. The sibling tools are obviously different, so the context is sufficient for basic selection.

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

B3.4/5.0

Disambiguation5/5

Each tool targets a distinct evidence domain (app reviews, ads, retail) and produces unique outputs. The only overlap is the general 'unsent actions' concept, but inputs and analysis types are clearly different.

Naming Consistency2/5

Tool names are inconsistent: three use multi-word hyphenated phrases, while 'pricing_info' uses snake_case. There is no uniform verb-noun pattern or consistent stylistic choice.

Tool Count4/5

Four tools is a reasonable count for a specialized server, though one tool is purely informational. The three functional tools cover distinct areas without feeling sparse or bloated.

Completeness4/5

Each analysis tool appears to cover its respective end-to-end workflow (evidence in -> findings/actions out). Minor gaps exist in broader decision-support capabilities, but for the declared bundle scope, the surface is fairly complete.

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