GoAI Moat Review Intelligence

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

With no annotations provided, the description carries the full burden. It discloses that the tool relies on DeepSeek semantic analysis, strictly uses only provided review text without fabricating data, and cites original quotes as evidence. It also reveals the automatic degradation behavior when the API key is missing. This is transparent about its dependencies and 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?

The description is concise, with the core purpose and outputs stated in the first two sentences, followed by key behavioral notes. Every sentence adds value, mentioning the analysis basis, evidence requirement, and fallback behavior. It is well-structured and front-loaded with the essential 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?

Given the tool's moderate complexity, the description covers the core functionality, output structure, data handling constraints, and fallback behavior. The output schema is present, so return values are covered. One minor gap: it doesn't specify how the analysis handles very large inputs or any limits, but this is not critical. Overall, it provides sufficient context for an agent to use 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 coverage is 100%, so the schema already documents all parameters. The description does not add significant extra meaning for the parameters, except perhaps implicit understanding of how they are used. It doesn't clarify formats for 'reviews' beyond what the schema provides (e.g., line-separated or JSON array). This meets the baseline for high schema coverage.

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: 'deeply analyzes a batch of reviews and outputs a structured VOC report'. It lists the specific output components (core pain points, feature requests, positive points, urgency, action suggestions) and explicitly distinguishes itself from a sibling via the fallback mechanism. This makes its purpose unambiguous and differentiates it from similar 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 implies when to use this tool (for deep semantic analysis) and mentions the fallback to a sibling when the API key is missing. However, it doesn't explicitly say when NOT to use it or suggest alternatives in other scenarios. The mention of the sibling in the fallback context provides some differentiation but lacks explicit usage 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It does add one genuine behavioral clue — 基于 DeepSeek 语义分析 — implying AI-generated, non-deterministic results. However, it does not disclose error/failure modes, external API dependency implications, or the approximate nature of the analysis beyond that single clause.

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 paragraphs with the purpose front-loaded in the first sentence and the usage recipe following directly. Every clause earns its place; the trailing DeepSeek note is a relevant behavioral disclosure rather than 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?

An output schema exists, so the description need not explain return values. The description covers purpose, usage recipe, and name semantics, while the schema documents all five parameters at 100% coverage. It is complete enough for correct invocation, though it omits input-size limits and handling of the lang parameter in different content languages.

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 interpretive value beyond the schema by mapping name_a to 你的产品 (your product) and name_b to 竞品 (competitor), and by explaining what the directional gaps mean strategically (must-fix points vs. selling points) — meaning the schema's terse name defaults do not convey.

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 — 对比两批评论 (compare two batches of reviews) — and enumerates the exact output types (A 有 B 无、B 有 A 无、差异化机会). This explicit two-batch comparative scope distinguishes it from siblings analyze_reviews and extract_voice_of_customer, which are single-batch analysis 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 典型用法 paragraph gives an explicit usage recipe: set name_a to your product's reviews and name_b to the competitor's, then interpret the two gap directions (competitor praised / you criticized = must-fix; you praised / competitor criticized = selling point). This is clear contextual guidance, but it never names sibling alternatives or states when not to use the tool, 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?

With no annotations, the description carries the burden and discloses key behaviors: deterministic word-frequency plus sentiment-dictionary algorithm, no external API calls, always available and reproducible, and local processing of user-supplied content. It could add details such as language scope beyond Chinese/English or limitations, but the core behavioral profile is well covered.

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 compact sentences each earn their place: outputs in the first sentence, behavior and algorithm in the second, and input format and guardrail in the third. The information is front-loaded 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 one-parameter tool with an output schema, the description provides enough to call it correctly: expected outputs, algorithm nature, input format, and the no-links constraint. Missing only explicit sibling differentiation and edge-case details like language or size limits, which are minor.

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 documents the single reviews parameter at 100%, so the baseline is high; the description complements it by clarifying accepted formats (one per line or pipe separated) and reinforcing that links are not accepted. This adds value beyond the schema without redundancy.

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 action and resource: analyzing a batch of product reviews to produce sentiment proportions, high-frequency keywords, and negative-review pain sentences. It does not explicitly contrast itself with the sibling analyze_reviews, but the stated deterministic, local algorithm and expected outputs make the tool's specific role clear.

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 clear context: the tool is for zero-cost, local, reproducible analysis of raw review text, and explicitly warns that it consumes content, not review links. It does not name alternatives or state when not to use it, 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.