My App Store Ranking

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior, so the safety profile is clear. The description adds key behavioral context beyond annotations: untracked apps return only a single recent day rather than a full window, which is valuable for setting 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?

The description is concise and well-structured: a dense first sentence states the core behavior, followed by a useful caveat and an example question. Every sentence earns its place with no redundancy.

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 output schema likely covers return values, and the description adequately conveys the tool's purpose and the untracked-app caveat. However, with five parameters and no schema descriptions, the lack of parameter guidance is a significant gap, especially for configuring countries/platforms and date windows.

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%, and the description does not explain individual parameters like countries, platforms, rangeDays, or end. The only implicit hint is that appStoreId identifies the app, but the format, defaults, and relationships between parameters are left undocumented, forcing the agent to infer from parameter names 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 explicitly states the tool retrieves a country grid for a single app over a date window, including previous-window comparison and per-platform breakdown. This is a specific verb+resource that clearly distinguishes it from siblings like get_chart_top or get_app_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?

It provides an example question ('Where does my app rank across Europe right now?') that signals the appropriate use case, and mentions that it works for untracked apps (with a caveat). However, it does not explicitly name alternatives or state when not to use this tool.

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?

The description adds substantial behavioral context beyond the annotations: results are ordered 'newest first', an unfiltered query 'will be truncated', and data only exists for 'apps the user tracks'. These are important operational characteristics that help set expectations and guide filtering, going beyond the read-only and idempotent hints already provided.

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 three purposeful sentences plus an example, with the core functionality stated first. Every sentence adds value: purpose, filtering caveat, scope limitation, and a concrete usage example. No redundant fluff.

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?

Given the tool has 7 parameters and an output schema, the description provides a good high-level context: it states the use case, ordering, truncation risk, data availability scope, and a real-world example. However, it could be more complete by explicitly linking parameters to their roles and describing pagination behavior, though the output schema may cover return structure.

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?

With schema description coverage at 0%, the description must compensate for parameter understanding. It mentions filter dimensions (country, platform, chart type, category) and the example implies time range ('over the last month') and app identification ('my app'), but it does not explicitly explain 'limit', 'sinceDays', or 'appStoreId' semantics. The truncation warning indirectly hints at 'limit', but overall parameter meanings are only partially conveyed.

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 states the tool returns 'raw rank observations, newest first' for trend questions, which is a specific verb+resource with an explicit purpose. It distinguishes from siblings by noting it applies to 'apps the user tracks' and focuses on historical data for trend analysis, unlike current-rank or chart-list tools.

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 provides clear guidance on when to use it ('for trend questions') and includes a concrete example question. It warns about the need to filter by country/platform/chart type/category to avoid truncation, and mentions the prerequisite that only tracked apps have history. However, it does not explicitly name alternative tools for when this one should not be used.

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 readOnlyHint=true, idempotentHint=true, destructiveHint=false, so the safety profile is clear. The description adds behavioral detail about output granularity (one row per app) and the comparison to the preceding equal-length window, which is not evident from schema alone. However, it does not cover potential edge cases like empty results or behavior when no tracked apps exist.

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 compact and well-structured: one sentence for output shape, one sentence for usage, and one example. Every sentence carries meaning; 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?

The tool has a full output schema and strong annotations, so the description doesn't need to explain return schema. It adequately covers the main use case and points to sibling for deeper analysis. However, the lack of parameter semantics is a gap in overall completeness, and the description could explicitly state that countries/platforms are optional filters to fully complete the picture.

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% — no parameter descriptions are provided. The description only alludes to a date window ('over the last 7 days') and mentions countries as a metric, but does not explain that countries, platforms, end, and rangeDays are optional filters for the summary. With zero schema coverage, the description should compensate by explaining parameter roles, but it does not, leaving the agent to infer from names.

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 states the tool returns one row per tracked app with specific metrics (countries charted, top 5/25 placements, change vs prior window, best position). It also explicitly contrasts with sibling tool get_app_country_ranks, differentiating portfolio-level summary from per-app drill-down.

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 gives explicit usage guidance: 'Use this for "how is my portfolio doing", then get_app_country_ranks to drill into one app.' It also provides an example question ('How did my apps do over the last 7 days?') that clarifies appropriate invocation.

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?

The description adds behavioral details beyond the annotations: it mentions the default to the most recent captured day and the flagging of tracked rows. These are useful behavioral traits that the annotations do not convey. With readOnlyHint, idempotentHint, and destructiveHint already provided, the description benefits from the low bar and adds relevant context, though it does not cover potential edge cases like data availability or pagination.

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 exceptionally concise: two sentences plus an example question. Every sentence provides essential information: the core functionality, the default behavior, the reference to another tool, and a practical example. No filler or redundant content.

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?

Given the output schema exists (so return values are already documented) and annotations cover safety, the description is complete. It covers the main use case, the default date behavior, the need for reference data, and offers an example. The complexity of 5 parameters is adequately addressed through the conceptual mapping and example, making the tool fully understandable.

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 schema description coverage is 0%, but the description compensates by mapping the query terms to the parameters: 'country, platform, chart type and category' directly align with country, platform, chartType, and genreId. It also directs users to list_reference_data for valid values and provides a concrete example with a country, platform, and category. However, it does not explicitly name the parameter identifiers or describe the date pattern, leaving some semantic detail to 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?

The description clearly states what the tool does: it reports who ranks in a given country, platform, chart type, and category on a given day, with the additional feature of flagging rows the user tracks. It uses a specific query verb ('Who ranks') and resource ('top chart'), and the example question reinforces the purpose. It also distinguishes itself from sibling tools by focusing on a single day/chart snapshot rather than historical trends or app overviews.

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 provides clear context by explaining what the tool returns and defaults to the most recent captured day. It also explicitly instructs to use list_reference_data for valid codes, which is a helpful pointer. However, it does not mention alternatives among sibling tools like get_app_history or get_apps_overview, nor does it specify when NOT to use this tool, so the usage guidance is implied rather than explicit.

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 this as read-only, idempotent, and non-destructive. The description adds substantive behavioral context beyond annotations by specifying what the report includes (portfolio summary, needs attention, wins, competitor movements, new entrants) and noting that it is the same analysis as the email report. This gives the agent a clear picture of the tool's output without needing to inspect the output schema.

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 sentences plus an example question. It is front-loaded with the core content, then gives the use case, then provides a concrete query example. Every sentence adds value, and the structure is efficient and highly scannable.

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 read-only tool with one optional parameter and an output schema, the description covers the report contents, the intended use case, and an example. It does not explicitly explain when to use 'weekly' vs 'daily', but the title and schema provide enough. The output schema likely covers return structure, so no further detail is needed.

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 input schema has one parameter 'frequency' with enum ['daily','weekly'] and default 'daily'. Schema description coverage is 0%, so the description must compensate, but it does not explicitly explain the frequency parameter. However, the tool title mentions 'daily or weekly report' and the description's overnight context implies daily usage, which helps. The parameter is simple and self-explanatory from the schema, so the lack of description is not a major gap.

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 states what the tool does: it provides the same analysis used for the user's email report, enumerating specific components (portfolio summary, needs attention, wins, competitor movements, new entrants). It also differentiates itself from siblings by positioning it as 'the best single call' for a scheduled overnight digest, making its scope distinct from more specific tools like get_app_country_ranks or get_app_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?

The description gives a clear usage context: it is intended for a scheduled 'what happened overnight' digest, and the example question 'What happened with my apps overnight?' reinforces when to call it. It does not explicitly exclude other tools or mention alternatives, but the context is strong enough to guide selection.

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 establish it as a safe, read-only, idempotent operation. The description adds useful context by disclosing the analytical behavior: it computes metrics across a date window and detects movement and new entrants. This goes beyond the schema and gives insight into what the tool produces, without contradicting any 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?

The description is two concise sentences plus an example question, front-loaded with the core purpose and followed by a concrete use case. Every sentence adds value and there is no redundancy.

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?

While the purpose and output (covered by an output schema) are clear, the description does not fully address the parameter space, especially since schema descriptions are missing. It gives a strong example but leaves questions about defaults (e.g., rangeDays default 30, limit default 25) and parameter meanings, making it partially complete for a 7-parameter tool.

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 schema has 7 parameters with 0% description coverage, placing the burden on the description to explain parameter meanings. The description only vaguely implies date-window (rangeDays) through 'date window' and uses 'French Music' in the example, but does not explain `end`, `limit`, `genreId`, or how parameters interact. This is insufficient for a schema of this size.

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 states the tool's function: 'Which apps dominated a chart over a date window' and enumerates specific metrics (median rank, best rank, days present, days at number one, movement against previous window, new entrants). It also gives a concrete example question, 'Who moved into the French Music top 10 this week?', which uniquely identifies this tool's purpose among siblings.

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 explicitly says, 'This is the tool for 'who moved into the top 10 this week',' providing a clear use case. It doesn't mention alternatives or when not to use it, but the example and phrasing give adequate usage guidance without naming exclusions.

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 communicate read-only, idempotent, and non-destructive behavior. The description adds context about output content: results are ranked by relevance, include frequency and margin of leading, and are derived from chart co-occurrence. This goes beyond the structured 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?

The description is two sentences: a concise definition and a clarifying example question. Every sentence contributes value, with no fluff or restatement of the title.

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 an output schema present, the description doesn't need to explain return values. It covers the core concept, methodology, and a usage scenario. Some minor gaps remain (e.g., chart scope or prerequisite of user apps), but overall it is adequate given the annotations and 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 0%, so the description carries the full burden for explaining parameters. It mentions no parameter explicitly and does not clarify how end, limit, countries, platforms, or rangeDays affect results. The example question implies a high-level use case but fails to map any parameter to its meaning.

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 defines the tool's resource (rival apps) and action (discover via chart co-occurrence and outranking). It distinguishes itself from siblings like get_leaders or get_chart_top by focusing on apps that outrank the user's apps, reinforced by the example question.

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 phrase 'Competitors discovered from the data rather than declared' implies when this tool is appropriate (data-driven discovery) versus a declared list. The example question provides a concrete usage scenario, but the description does not explicitly name alternative tools or formal exclusion conditions.

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 readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the description does not need to restate those. It adds valuable context about the return fields and the prerequisite role of this tool, enhancing the agent's understanding beyond the structured 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?

The description is two concise sentences plus an example question. The first sentence delivers the core function, the second provides a clear usage directive, and the example clarifies the intent. No redundancy or unnecessary detail.

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 0-parameter list tool with an output schema, the description fully covers what the tool returns and when to use it. The presence of an output schema means return-value details are already documented, so the description is complete for the agent to select and invoke the tool 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?

This tool has zero parameters, making schema coverage trivially 100%. The description does not need to explain parameters, but it does highlight the appStoreId as the key linking value for other tools, adding conceptual meaning to the tool's role in the workflow.

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 states the tool returns every tracked app with specific fields (store id, store, platforms, categories, ownership status). This distinguishes it from sibling tools like get_app_history or get_chart_top that operate on specific apps. The verb 'Returns' is precise and action-oriented.

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 explicitly instructs to 'Call this first' and explains that other tools identify apps by the appStoreId returned here, providing clear when-to-use guidance. However, it does not explicitly mention when not to use it or name alternative tools, so it falls slightly short of a perfect score.

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. The description adds crucial behavioral context beyond annotations: failed calls still consume quota, and this tool is the authoritative source for valid enum values. This is valuable operational information not present in the structured fields.

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 sentences with no redundant words. The first sentence immediately identifies the resource and its scope, and the second delivers a practical usage warning. Every word 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 zero-parameter tool with no notable complexity, the description fully covers what the tool does, when to use it, and why it matters (quota impact). No additional context is needed.

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, so schema coverage is trivially 100%. The description adds semantic value by explaining that the output provides valid values for parameters of other tools (e.g., country codes, category IDs), which is essential for correct parameter usage across the API.

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 states the tool's purpose: it lists 'the enum values every other tool accepts,' specifically countries, platforms, chart types, and categories. This distinguishes it from all sibling tools, which are data retrieval operations, not reference data.

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 instructs when to use: 'Call this before guessing a country code or category id.' It also provides a strong rationale—invalid parameters fail and count against the daily quota—making the usage context very clear.

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