GoAI Moat Product Selection & Niche Research

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

With no annotations, the description carries the burden. It discloses that the tool returns a framework (not live validation) and specifies the dimensions it contains, which is meaningful behavioral context. It could still state whether thresholds are static or data-derived, but the 'framework' wording already sets that expectation.

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 packs the input condition, output resource, and three output components with no filler. Every phrase 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 low-complexity tool with two documented parameters and an output schema, the description is nearly complete: it covers purpose, input, and return content. The only notable gap is the lack of explicit sibling differentiation, which is minor given the stated framework contents.

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 100%, so the parameter descriptions already document 'category' and 'market'. The description only echoes the category input and adds no detail about the 'market' parameter or value formatting, so it stays at the baseline for covered schemas.

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 ('返回') and resource ('细分市场验证框架') and enumerates the framework's contents (what to validate, thresholds, red flags), so an agent knows what the tool produces. It is distinct from 'score_product_idea' and 'seasonal_launch_calendar', but it does not explicitly contrast itself with them.

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 opening '给定品类' provides a clear trigger condition, so the when-to-use is implied. However, the description offers no guidance on when to prefer this over score_product_idea or when not to use it, leaving the choice among siblings to inference.

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 discloses the core behavior: seven-dimensional weighted scoring and an output of feasibility plus first validation steps. But it does not explain how weights work, what score thresholds mean, how optional parameters affect the result, or the interpretation of the 'can it be done' verdict. This is adequate but has clear gaps.

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 a single compact sentence with no filler. It front-loads the core action and includes the most important output expectation, making it efficient and easy to parse.

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 input schema is rich and fully documented, and presence of an output schema covers return-format details. However, the description alone does not give enough guidance on when to use the tool or how the scoring behavior is calibrated, so the overall context is not fully complete for an 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?

Schema description coverage is 100%, so the parameter semantics are fully documented in the schema. The description adds no additional parameter-level meaning beyond the schema, so the baseline score of 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?

The description uses a specific verb-resource pair ('给一个产品想法做七维加权评分') and names concrete outputs ('能不能做 + 要做先验证什么'). It is clear about what the tool does and differentiates it semantically from the sibling tools by focusing on scoring, though it does not explicitly name or contrast those 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 implies usage context: it is for scoring a product idea and producing a feasibility verdict with first validation steps. However, it gives no explicit guidance on when to choose this over niche_validation_framework or seasonal_launch_calendar, and no exclusions or alternative 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It does indicate a read-only operation by saying '返回' and describes the output content (calendar + lead times), but it does not mention data sources, freshness, or any limitations. For a simple query tool this is adequate but minimal.

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 compact sentence front-loads the deliverable and ends with the use case. There is no filler, repetition of schema details, or unnecessary elaboration.

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 only one optional parameter, full schema coverage, and an output schema available, the description provides the essential behavior and purpose. The main gap is the lack of explicit sibling differentiation, but the tool is simple enough that an agent can invoke it correctly based on this description.

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 already fully documents the single parameter (market) with its default and valid values (US/EU/JP). The description adds no new semantic detail beyond restating '目标站点', so with 100% schema coverage the baseline of 3 is appropriate.

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 ('返回') and names the deliverable: a seasonal demand rhythm calendar plus lead times, with an explicit goal ('指导什么时候上架'). This clearly distinguishes it from the sibling tools, which focus on niche validation and product idea scoring.

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 '指导什么时候上架' gives a clear intended use case for launch timing, but the description never explicitly contrasts this tool with its siblings or states when not to use it. No exclusions or alternative routing are provided, so 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.