App Store Opportunity Tracker

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

Annotations already declare readOnly/idempotent/non-destructive/openWorld, so the safety profile is covered. The description adds genuinely non-obvious behavior beyond that: the answer is fed by a background scan and "coverage null means it has just started," which tells the agent results may be incomplete on first call — a real operational caveat.

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?

Front-loaded with the trigger condition, then the parameter list, then the return shape. It is a dense single paragraph that is slightly overlong (the proof.recentReviews/proof.ratingsRising detail is heavy), but nearly every clause carries information an agent would otherwise have to guess.

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?

An output schema exists, so return values need not be spelled out, yet the description still names the key signals (ratingsRising, recentReviews, terms with source, 0–100 score) and warns about coverage null. Combined with annotations covering safety, nothing needed to call this correctly is missing.

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 every parameter is already documented, including defaults and ranges. The description only restates the role of genre/min_ratings/min_days and omits limit entirely, adding little meaning beyond the schema — the baseline 3 for a fully-covered schema.

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?

States a specific, unusual resource — apps that are no longer updated but still get ratings/searches — with an example query. That resource is unmistakably distinct from siblings like app_tracker_charts, app_tracker_lookup or app_tracker_opportunities, so an agent can route correctly without opening a schema.

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?

"Use this when the user wants app ideas from apps that are no longer updated but still found and rated" gives an explicit trigger condition plus a concrete example query. It does not, however, state when to prefer a sibling (e.g. app_tracker_opportunities) or any exclusion, so it stops short of full routing guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

Annotations already declare readOnly, idempotent, non-destructive and open-world behavior, so the safety profile is covered. The description adds genuine behavioral context beyond that: snapshot comparison semantics ('change since an earlier snapshot'), the mover categories returned, and that movers.since may be null when no prior snapshot exists. It does not cover rate limits or cost, which keeps it from a 5.

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?

Front-loads the trigger condition, then parameters, then return shape, and sentences are information-dense with no filler. Slightly long as a single block, but every sentence carries content an agent can act on.

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?

With full schema coverage, rich annotations and an output schema, the essentials are already covered, and the description adds trigger guidance and snapshot semantics on top. Nothing needed to call it correctly is missing, though the return-shape recap partly duplicates the output schema.

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 the schema already documents store/country/chart/genre/limit including formats, defaults and enum meanings. The description only lists the parameter names ('Pass store, country, chart, genre and limit') without adding syntax, defaults, or constraints beyond the schema, so the baseline 3 applies.

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?

States a concrete verb and resource ('Read a top chart with movers') and grounds it in sample user utterances ('top free Productivity apps in the US', 'what is climbing the Health & Fitness chart?'). An agent can distinguish this chart-reading tool from siblings like app_tracker_lookup or app_tracker_keywords without opening any schema.

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?

Opens with an explicit trigger condition ('Use this when the user asks what is on or climbing an App Store or Google Play top chart') plus two example phrasings, which is strong context. It stops short of naming when NOT to use it or pointing to a specific alternative sibling, so it falls short of a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

Beyond the readOnly/idempotent annotations, it discloses scope (worldwide App Store, last 30 days), currency semantics (gross USD before Apple's commission, no ad revenue), output shape and the meaning of each estimation method, including that rank_bound is only an upper bound. It also warns that growth fields are null until enough history exists, which is exactly the kind of caveat an agent needs.

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?

Front-loaded with the use case and trigger example, then the parameter, then the return contract; every clause carries information. The single long sentence enumerating return fields is dense but not padded.

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?

The description covers trigger, input, output semantics, estimation methods, caveats and scope limitations, so an agent can call it correctly without further inference. Since an output schema exists, the return-value detail is redundant but harmless, and nothing needed for correct invocation is missing.

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 both parameters are already documented, and the description mostly repeats the 'app id or link' guidance. The one substantive addition is reinforcing that country only locates the app while the estimate is always worldwide, which the schema also states.

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?

States a specific verb (estimate) and specific resources (revenue and downloads) for an App Store app, so the agent knows exactly what it produces. It does not explicitly contrast itself with siblings like app_tracker_lookup, so differentiation is inferred rather than stated.

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?

Gives an explicit trigger condition ('Use this when the user asks how much an App Store app earns or how many downloads it gets') plus a concrete user-utterance example, which is strong routing guidance. It names no alternatives or exclusions, so it falls short of a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

Annotations already cover read-only, idempotent, open-world semantics, and the description adds real substance on top: results are live, the difficulty score is a 0-100 heuristic derived from rating volume and name matches rather than Apple search volume, and the response includes a top-10 list of apps rated under 4.2. It does not mention rate limits or result-count limits for the live lookup.

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?

A single dense paragraph that leads with the selection trigger, then the required inputs, then return contents and the difficulty caveat. Every sentence carries information, though the return-value enumeration is somewhat long given an output schema exists.

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 two-parameter read tool with full schema coverage, an output schema, and annotations covering the safety profile, the description supplies the trigger, the inputs, and a caveat about how to interpret the difficulty metric. Nothing an agent needs to invoke it correctly is missing.

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%, and both parameters (term, country) are already documented with format, pattern and the us default. The description's 'Pass term and country' adds no syntax or constraint detail beyond the schema, so the baseline 3 applies.

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?

Names a specific resource (live iPhone App Store search results for a term) and the exact user intent it serves, with a concrete example query. An agent can distinguish it from sibling tools like app_tracker_estimate or app_tracker_charts, which serve different questions, without opening any schema.

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?

Explicitly states the triggering situation ('when the user asks who ranks for a search term or how crowded it is') and gives a sample phrasing. It stops short of naming which sibling to use instead when the user wants volume estimates or historical charts, so the routing guidance is good but not exhaustive.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, open-world), but the description adds real behavioral context: the response shape for batch calls, that reports come back 'in the order asked', and that unreadable apps land in a 'failed' list with error codes. That partial-failure semantics is genuinely useful and not derivable from the annotations.

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?

It is a dense block but front-loads the trigger and its examples, then moves to parameters and return values in a logical order. Nearly every clause carries information, though the traffic-heavy example quotes make it longer than strictly necessary.

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 read-only lookup with rich annotations and an output schema, the description covers the trigger, both input modes, shared country/store/reviews behavior, and partial-failure handling. Nothing an agent needs in order to select and invoke this tool correctly is missing.

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 the schema already documents each parameter including the app vs. apps mutual exclusion and the country default. The description mostly restates what the schema says (id/link/package forms, up to 5 apps, country), adding little new syntactic or semantic detail. Baseline 3 applies when the schema does the heavy lifting.

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 states a concrete action and resource: tearing down one or up to five competitor apps and returning a per-app report. It is far more specific than a restatement of the name. It does not, however, explicitly say how it differs from overlapping siblings such as app_tracker_estimate, even though it advertises an 'estimate and a Stale Winner score' that could be confused with that tool.

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?

It gives a clear trigger ('when the user asks about one competitor app') with sample questions, and explains the single-app vs. multi-app path ('pass app ... or apps with up to 5 of them to compare competitors in one call'). No explicit when-not or named alternative tool is provided, so it stops short of a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

Annotations already declare readOnly/idempotent/non-destructive, so the safety profile is covered. The description adds real value beyond that: the scan covers all 100 chart apps regardless of limit, the score is 0-100 with 75+ as high, and the explicit caveat that scores rank opportunities rather than prove a market.

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?

Front-loaded with the usage trigger, then inputs, then return shape and caveat, in a compact three-sentence block. The enumeration of flags and estimate ranges is slightly list-heavy but each item carries information an agent would otherwise have to guess.

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?

With rich annotations and an output schema present, the description covers purpose, inputs, and interpretation caveat, which is more than required. The one gap is sibling routing against app_tracker_abandoned and app_tracker_charts, which the agent must resolve on its own.

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 all six parameters are already documented with defaults, ranges, and enum meanings. The description only says "pass the chart inputs and min_score," adding no syntax or semantics beyond the schema, which is the expected baseline here.

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 gives a concrete verb+resource (scan a chart and score each app for Stale Winner signals) and pairs it with a realistic user phrasing. It is clear on its own, but it never names the sibling it overlaps with most (app_tracker_abandoned), so an agent cannot fully separate the two from the text alone.

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?

"Use this when the user wants app ideas or weak incumbents in a chart" plus a sample query gives a clear triggering condition. It stops short of exclusions or alternatives, e.g. when to prefer app_tracker_charts or app_tracker_abandoned instead, which is the missing half of a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive, so the safety profile is covered. The description adds genuinely useful behavioral context beyond that: the result set is capped ('up to 60 by rating count') and totals are computed by adding the ranges, which tells the agent about aggregation semantics and limits.

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?

Three tightly packed sentences: usage trigger first, then input options, then return shape. The example query earns its place by disambiguating the intent, and there is no filler.

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 read-only aggregate tool with an output schema present, the description covers when to call it, the mutually exclusive inputs, the 60-item cap, and the aggregation behavior. Nothing an agent needs in order to invoke it correctly is missing.

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 the 3 baseline applies. The description goes slightly beyond by clarifying that 'app' can be any App Store app belonging to the developer (not necessarily a specific one) and that developer is an id, which makes the anyOf either/or contract clearer than the schema alone.

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 states a specific verb and resource ('estimate a developer's apps earn together') and explicitly positions itself against the sibling app_tracker_estimate by noting it returns 'the same revenue and download ranges as app_tracker_estimate, plus totals'. An agent can distinguish the publisher-level aggregate from the single-app estimator without opening either schema.

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?

It gives an explicit trigger condition ('when the user asks what a developer's apps earn together'), a concrete example query, and routing guidance for the alternative inputs ('Pass app... or developer'). The reference to app_tracker_estimate implicitly names the single-app alternative, so selection is unambiguous.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

Annotations already declare readOnly, idempotent, and non-destructive, so the safety profile is covered. The description adds genuinely new behavioral context beyond that: the tool is pollable (pending until a reply arrives, then answered) and carries an explicit trust boundary warning that reply content is data, not instructions — a meaningful prompt-injection disclosure.

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?

Three tight sentences: purpose first, then polling behavior, then the trust caveat. Every sentence carries distinct information and nothing is padded.

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 single-parameter read tool with an output schema and full annotation coverage, nothing an agent needs is missing. The polling semantics and the injection warning go beyond what structured fields provide, closing the remaining gaps an agent would otherwise have to discover by trial.

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?

Only one parameter exists and the schema already documents it fully (coverage 100%, including the ^fb_[0-9a-f]{32}$ pattern and the note that submit_feedback returned it), so the baseline of 3 applies. The description repeats the ticket-id linkage but adds no format or validation detail beyond the schema.

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?

States a specific verb (Read) and resource (maintainers' reply to feedback), and ties it causally to the sibling tool submit_feedback via the ticket id. An agent can distinguish it from submit_feedback, screen_token, and wallet_identity without opening any schema.

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?

Clearly establishes the precondition: this is used after submit_feedback, with the ticket id that call returned. It does not state any exclusions or alternative ways to retrieve feedback status, but the usage context is unambiguous.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

Adds substantial context beyond the annotations: it returns a ticket id, feedback is explicitly not a chat and nothing in it is executed, and links/emails/phone numbers are stripped with nothing stored about the caller. That privacy and no-execution disclosure is genuinely useful given openWorldHint=true and readOnlyHint=false.

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?

Front-loads what the tool sends, then what to pass, then what it returns, in three dense sentences with no filler. Every clause carries actionable information.

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?

Covers purpose, the enumerated kinds, the optional tool/error inputs, the 1000-character bound, the return value (ticket id), and privacy handling. With an output schema present, nothing further is required for an agent to call it correctly.

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 the schema already documents all four parameters including the kind enum. The description restates the message length limit and the optionality of tool/error ('if you know them') but adds no syntax or format detail beyond the schema, making the baseline 3 correct.

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?

States a specific verb and resource ('Send feedback, a bug report or a tool request to the maintainers of the tools on this server') and enumerates the concrete situations (missing tool, stale data, broken/wrong answer). It is clearly distinguishable from the read-side sibling get_feedback_reply.

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?

Gives clear triggering conditions for each kind (need_tool, need_data, bug, other) and tells the agent to include the tool name and error text when known. It does not explicitly name the alternative get_feedback_reply or state when NOT to use this tool, so it falls short of full routing guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.