PastCharts

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

No annotations are supplied, so the description carries the full burden. It discloses the shape of the result (all saved charts plus daily ranks), which implies a read-only, potentially large historical payload, but says nothing about permissions, date-range limits, pagination, or volume — meaningful gaps for a history endpoint.

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 short sentences with zero filler; the resource and payload are front-loaded and the prerequisite follows immediately. Nothing can be removed without losing 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?

For a simple two-parameter read tool with no output schema, the description covers what is returned at a high level, which is the main thing missing otherwise. However, the semantics of the store parameter and any constraints on history depth or payload size are absent, leaving gaps an agent would hit at call time.

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 0%, so the description must compensate. It does add real meaning for id by naming search_apps as the source, but it says nothing about store beyond the self-explanatory enum values, leaving unclear whether store must match the store of the id returned by search_apps.

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 names a specific resource (every saved chart an app has been on) and the payload (daily ranks), which is enough to distinguish it from siblings like get_chart or get_top_trends. It falls short of a 5 because it doesn't explicitly contrast itself with get_chart, which an agent could reasonably confuse with chart history.

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?

"Get the id from search_apps" gives a concrete prerequisite and call sequence, so the agent knows it cannot invoke this tool cold. There is no explicit when-not guidance or pointer to get_chart for chart-level detail, so it stops short of full alternative routing.

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?

With no annotations at all, the description carries the full disclosure burden. It does add genuine behavioral context — this is a 'saved' snapshot and data begins at 2026-11-06 — but it says nothing about what happens when no snapshot exists for a requested date, whether results are paginated, or what authentication/limits apply.

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 clause with the resource up front, the scoping dimensions next, and the data-availability bound last. Nothing is redundant or 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 6-parameter read tool with no output schema and no annotations, the description covers the query dimensions but not the payload. It does not convey that the result is a ranked list of apps with positions, or how the default limit of 25 affects shape, leaving the agent to discover the return format.

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 67%, with the date, limit, country, and category parameters already documented in the schema, so the baseline of 3 applies. The description reinforces the country/day/store dimensions but adds no format or constraint detail beyond what the schema 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?

The description names a concrete resource (a saved top chart), the two stores it covers, and the country/day scope, which is enough to tell it apart from siblings like get_app_rank_history and search_apps. It is a noun phrase rather than a verb, so the retrieval action is implied rather than stated, but the subject matter is unambiguous.

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?

There is no when-to-use guidance and no mention of the sibling tools (get_app_rank_history, get_top_trends, search_apps) that an agent could confuse this with. An agent must infer from the name alone that this returns a ranked chart listing rather than per-app rank history.

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?

No annotations are supplied, so the description carries the full behavioral burden. It discloses the accepted period formats, which is real added context, but says nothing about how many trends are returned, ordering/ranking basis, whether results are cached, or what the response shape looks like. For a read-only lookup tool it does not contradict anything, but it stops short of the disclosure a no-annotation tool ideally 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?

A single front-loaded sentence with zero filler; the parenthetical format note is the highest-value piece of information and is placed immediately after the field it clarifies.

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 no output schema, the description covers what the tool returns and how to format the period. The remaining gaps — country value format and result cardinality/ordering — are the ones an agent would need in order to call it correctly on the first try.

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 0%, so the description must compensate for both parameters. It does explain 'period' precisely (YYYY-MM for a month, YYYY for a year), but 'country' is left totally undefined — no indication whether it takes an ISO code, an English name, or another format. Half the parameters are still undocumented.

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 resource and qualifier: 'Biggest Google trending searches in a country for a month or year'. The 'in a country' scope implicitly separates it from get_world_trends, but the description never names that sibling or explains how it differs from get_trending_searches, so the differentiation is left to inference.

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 the use case (top-N trends for a specific country and time period) but gives no explicit when-to-use rule, no exclusions, and no pointer to the sibling tools that also return trending-search data. An agent must guess at the boundary between this and get_trending_searches.

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?

With no annotations, the description carries the full burden and does surface two real behavioral facts: results are historical rather than live, and coverage has gaps on some days. It says nothing about authentication, rate limits, result ordering, or what a response looks like, which leaves the read behavior only partly characterized.

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 compact sentences with the scope constraint front-loaded and no filler. The phrase 'from 2024-11-28' is ambiguous, though — it reads as either the earliest available date or a data vintage, and the reader has 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?

For a three-parameter read tool with full schema coverage and no output schema, the essentials are present, and the missing-days caveat is valuable. However, the 2024-11-28 anchor is left unexplained and the return content is never characterized, so an agent still lacks a full picture of what to expect.

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 format, defaults, and ranges for date, limit, and country. The description adds only the framing that the day must be in the past, which slightly reinforces the date parameter but goes no further.

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 resource and verb-less but clear operation: top Google trending searches scoped to one country on a past day. The country scope distinguishes it from get_world_trends by implication, but the description never names that sibling or get_top_trends, so an agent must infer the split.

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?

Implies usage (historical, country-scoped lookups) and warns that some days are missing, which shapes when a caller can rely on it. There is no explicit routing guidance versus the closely named siblings get_top_trends and get_world_trends, so the when-to-use decision remains inferred.

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?

With no annotations, the description carries the full burden; it does disclose the key behavioral trait that results are ranked by number of countries, which is meaningful. However, it says nothing about the return shape, ordering, result limits, or whether historical coverage is bounded.

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?

One sentence, front-loaded with the ranking semantics and ending with the accepted input formats. Nothing is wasted or buried.

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 one-parameter read tool with no output schema and no annotations, the description covers what the tool returns conceptually and how to format the input. The remaining gap — result size and ordering details — is modest at this complexity level.

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 coverage is 0% — the single 'period' parameter is an undescribed string — so the description must compensate, and it does by specifying both accepted formats (YYYY-MM-DD for a day, YYYY for a year). It still does not state timezone, validity bounds, or what happens with a malformed value.

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 resource and ranking criterion: searches that trended in the most countries, on a given day or year. This distinguishes it in substance from siblings like get_top_trends and get_trending_searches, though it never names them explicitly.

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?

There is no when-to-use guidance and no mention of the closely related siblings get_top_trends, get_trending_searches, or get_chart, so an agent must infer from the phrase 'most countries' alone whether this or a sibling is the right call. Usage is implied rather than stated.

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?

No annotations are supplied, so the description carries the full disclosure burden, and it provides almost none: no indication of read-only nature, return shape, size of the catalog, pagination, or whether the result links to the sibling tools. Only the 'live data' phrase hints at freshness semantics.

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 compact sentence with the catalog scope front-loaded and no filler. It is a fragment rather than a complete sentence, but nothing is wasted.

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 zero-parameter, no-output-schema tool, the description covers what the catalog contains but not what a call yields (scraper names, IDs, run endpoints?) or how those results relate to the sibling tools. That missing return-shape context is the main gap.

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 takes zero parameters, so there is nothing for the description to explain beyond the schema; baseline 4 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?

The description identifies the resource ('All Wulfcare scrapers on Apify') and enumerates the data domains covered, which helps separate it from the sibling data-fetching tools. However, it is a noun phrase with no verb — it never says the tool lists or returns these scrapers, leaving the actual action implicit in the name.

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?

There is no statement of when to call this versus the siblings (get_top_trends, search_apps, etc.), nor any prerequisite or exclusion. An agent must infer that this is a discovery/catalog call from the name alone.

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?

With no annotations, the description carries the full behavioral burden. It usefully discloses that results include store, id, and best saved rank, but it does not describe matching behavior, result limits, pagination, or explicitly confirm that the operation is read-only.

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 tight sentences with no filler. It front-loads the search action and then states the returned fields, so every sentence 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?

For a simple one-parameter search tool with no output schema, the description covers the core action and return fields. It remains light on result limits, matching semantics, and pagination, which an agent may need to invoke it confidently.

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 0%, so the description must compensate for the single 'query' parameter. It does so partially by saying the query can be an app name or developer, but it gives no matching, formatting, or example details.

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: find apps in the archive by name or developer. It also names the returned fields, making the tool's purpose clear against siblings like get_app_rank_history. Sibling alternatives are not explicitly addressed, so it falls just short of a 5.

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?

Usage is implied by 'Find apps in the archive by name or developer,' which tells the agent this is the app-search tool. It does not state when to use it instead of siblings such as get_app_rank_history or get_chart, nor does it give exclusions.

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