minradar

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

With no annotations, the description carries the full burden. It discloses what the explanation covers (formulas, weights, limitations) but doesn't explicitly state that the tool is read-only, produces no side effects, or how the explanation is generated. For a zero-parameter informational tool this is acceptable, but not fully transparent.

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 sentences, each earning its place: the first states the core action and content, the second gives a clear trigger for use. No filler, front-loaded, and perfectly sized for the purpose.

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 output schema, the description adequately covers purpose and when to use. It doesn't specify the response format, but it's reasonable to infer a textual explanation. A slight ambiguity about what 'the data' refers to is acceptable given the sibling context.

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 and the schema is empty, so schema description coverage is effectively 100%. The baseline for 0-parameter tools is 4, and the description adds no parameter confusion while still signaling that no inputs are needed.

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 ('Explain') and resource ('scoring formulas, weights, and honest limitations of the data'), which is unambiguous. It is clearly distinct from the sibling tools, which all focus on market research actions rather than explaining methodology.

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 'Use when a user questions the numbers,' providing a clear trigger condition. It doesn't name alternatives or exclusions, but the sibling tools are not genuine alternatives since none explain scoring methodology, so the guidance is sufficient 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?

No annotations are provided, so the description carries the burden. It discloses that the tool returns a ranked table with specific fields ('tier, score, rating and scale'), which gives the agent a clear expectation of the result. It also explains the core behavioral premise ('large user base but clearly unhappy'). For a read-only ranking tool, this is adequate transparency, though it omits any mention of data sources, latency, or edge cases.

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 sentences carry the core purpose, the output format, and the usage directive. Every sentence earns its place, and the most important information is front-loaded. There is no fluff or 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 description is complete for a discovery tool: it names the output shape (ranked table with tier, score, rating, scale) and the intended position in the workflow (first step). With 7 optional parameters all documented in the schema and no required inputs, the missing details are not critical for invocation, though it could briefly mention that all filters are optional.

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 baseline is 3. The description does not add any parameter-level details because the schema already documents each parameter with descriptions, defaults, and examples. No additional semantic value is provided by the description beyond what the schema offers.

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 ('Rank') and resource ('App Store opportunities'), and additionally specifies the distinguishing criterion: apps with a large user base and unhappy users (unmet demand). It also positions itself as the entry point tool, setting it apart from siblings like explain_method or get_opportunity_brief.

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 'Use this first when deciding what app to build', which gives clear usage context. It does not name alternative tools or give when-not-to-use conditions, but the 'first' instruction effectively signals the intended workflow among the sibling tools.

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 provided, the description carries the behavioral burden. It adds useful transparency by listing what the spec includes ('wedge, verbatim complaints, verified open-source accelerators, verification plan') and by noting output is formatted for a specific agent. It does not mention prerequisites, side effects, or whether any external data is fetched.

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 tight sentences with no filler. The main purpose is front-loaded, and the second sentence efficiently enumerates the deliverable's key sections.

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 gives enough to understand the deliverable's substance and target format, but there is no output schema and no explanation of how parameters affect the generated brief, such as storefront's role or what happens when target is omitted. Sufficient for basic invocation, but gaps remain.

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 67%, and the description reinforces the 'target' parameter by naming its enum values '(codex, claude, or cursor)' and linking them to agent formatting. It adds little beyond the schema for app_id and storefront, which are already described.

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 verb and outcome: 'Produce a ready-to-build product specification for an opportunity' and lists the included components. However, it does not explicitly distinguish itself from the similarly named sibling get_opportunity_brief, so an agent must infer the difference.

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 when the tool should be used: when a ready-to-build specification for an opportunity is needed and a target AI coding agent is known. It does not explicitly state when not to use it or how it compares to siblings like get_opportunity_brief or find_unmet_demand.

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 behavioral burden. It clearly discloses what the call returns and implies a read-only inspection operation. It doesn't discuss auth, failure modes, or output format, but for a retrieval tool the returned contents are the key behavioral information.

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 tight, purposeful sentences. The first front-loads the full output contract with a colon-separated list; the second gives workflow context. There is no repetition or 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 3-parameter tool with no output schema, the description is largely complete: it lists the return contents and the schema documents all parameters. Minor gaps, such as output format or invalid app_id handling, are acceptable for a read-oriented dossier 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?

Schema description coverage is 100%, so the baseline is 3. The description adds little beyond the schema: it reinforces that the target is one app and that it follows from find_low_hanging_fruit, but app_id, storefront, and include_github are already fully described in 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 the tool's action: retrieving a full evidence dossier for one app, and enumerates its contents: score breakdown, diagnosis, complaint themes, verbatim quotes, review provenance, and open-source accelerators. This distinguishes it from broader sibling tools like market_stats or generate_app_brief.

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 explicit usage context: 'Use after find_low_hanging_fruit to inspect a specific candidate.' This tells an agent when in the workflow to invoke it, though it does not explicitly compare against nearby alternatives like explain_method or generate_app_brief.

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 provided, so the description carries the full burden. 'Report' implies a read-only operation, and the last-ingest-run detail adds freshness context. However, it does not explicitly confirm the absence of side effects or describe any behavioral limitations.

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 that covers the essential content with no filler or redundancy. Every phrase adds useful 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 zero-parameter tool, the description names the key report contents, but 'local' is ambiguous and the output format is not described even though no output schema exists. The description is minimally viable but leaves some inference to the agent.

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 and the schema coverage is 100%, so the baseline is 4. No parameter explanation is needed because there is nothing to configure.

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 uses a specific verb ('Report') with a clear resource: local data coverage across apps, reviews, and chart snapshots, plus the last ingest run. This content clearly separates it from sibling tools like find_unmet_demand or explain_method.

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 guidance on when to use this tool versus alternatives, such as checking data freshness before running a market analysis. The description only states what it reports, not the context or conditions for selecting it.

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 provided, so the description carries the behavioral burden. It adds meaningful selection criteria ('maintained', 'commercially reusable', 'license posture') and frames the operation as non-destructive find/check. However, it does not specify output shape, license detail, or any operational constraints, so the disclosure is only partial.

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 terse sentences front-load the core purpose and immediately follow with use cases. No redundant wording or restatement of the name.

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 search tool with no output schema, the description covers what it finds and why you'd use it. It omits details such as response format and exact license fields, but an agent can infer call intent from genre/theme/limit.

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?

Two of three parameters have schema descriptions with examples; limit only has a default and no semantics. The description connects the tool to niche and safe-shipping but does not explain how genre/theme/limit interact, so it adds little over 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 names a precise verb-plus-resource ('Find maintained, commercially reusable open-source projects') with a scope ('for a niche') and a distinctive output characteristic ('with license posture'). It also gives concrete application purposes, making it unambiguous even among market-research 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?

It states explicit usage contexts: 'avoid rebuilding infrastructure' and 'check whether a dependency is safe to ship.' It does not name alternate tools or negative conditions, so it lacks the explicit when-not-to-use guidance required for 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?

With no annotations, the description carries the burden of explaining behavior, and it does by describing a non-mutating ranking/analysis operation and listing the evidence signals considered. It does not discuss return format, latency, or auth, but 'Rank... by evidence' and 'before building' make the read-only analytical nature clear.

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 sentences, with the tool's action and evidence criteria front-loaded and the usage instruction right after. Every clause adds information; there is no padding.

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 gives purpose and timing but, with no output schema and no parameter explanations, it does not tell an agent what the ranked result looks like or how the optional inputs affect it. For a 3-parameter tool with zero schema descriptions, this is a meaningful completeness 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?

Schema description coverage is 0% and the description never mentions limit, storefront, or min_rating_count, nor how they shape the ranking. The parameter names and defaults in the schema are the only source of meaning, so the description fails to compensate for the missing schema documentation.

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 opens with a specific verb-resource pair: 'Rank app categories by evidence that users already pay inside them.' It then enumerates concrete evidence signals (Top-Grossing chart presence, upfront charges, paywall language, review depth), making the tool's job distinct from siblings like find_unmet_demand or market_stats.

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 explicitly says when to call it: 'Call this before building to confirm a market monetises.' It does not name sibling tools or give when-not-to-use conditions, so it falls just short of a full 5.

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