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

With no annotations, the description carries the full behavioral burden and delivers: public read-only with no auth required, and the non-obvious error behavior that a missing id returns an error message rather than an empty result. This is exactly the context an agent cannot infer from 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?

Three tight sentences with zero filler: purpose front-loaded, then the auth/safety note and error semantics, then the routing rule. Every clause 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 single-param read tool with no output schema, the description supplies the return payload (specs, price, stock, MOQ, merchant name), the auth posture, and error behavior, leaving nothing an agent needs to call 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% and the single id parameter is already documented as coming from a search_stocks result. The description restates 'by id' and reiterates the search_stocks provenance, adding no syntax or format detail beyond the schema, so the baseline 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?

States a specific verb and resource (get full details of one product by id) and enumerates the returned fields, making it clearly distinct from siblings list_categories and search_stocks. An agent can select it without opening the schema.

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 names the precondition (use when you already have a product id from search_stocks) and routes keyword-based discovery to search_stocks instead. Both the when and the when-not are 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?

With no annotations, the description carries the full burden and does disclose key traits: 'Public read-only, no auth required' plus 'live inventory structure,' which tells the agent the counts are current rather than cached. It does not mention pagination, result size limits, or response shape, which is a gap for a no-annotation tool.

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 short sentences, no redundancy, and the core capability is front-loaded ahead of the auth note and the routing to search_stocks. Every sentence adds 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 zero-parameter, no-output-schema read tool, the description covers purpose, safety profile, data freshness, and routing. The only real omission is what a returned category entry looks like beyond the mention of per-category counts, which an output schema would normally carry.

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 (schema coverage 100%, empty properties object), so there is nothing for the description to disambiguate; baseline 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?

States a specific verb and resource ('List all product categories') and narrows the scope to categories that 'currently have in-stock items, with per-category quantity counts.' This is clearly distinguishable from get_stock and search_stocks, which operate on individual products.

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?

Gives two explicit use cases (overview, and picking a valid category value for search_stocks) and names the alternative tool for a different need: 'use search_stocks to drill into products of one category.' The when-to-use and when-to-use-something-else are both 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?

With no annotations present, the description carries the full burden and does so: public read-only, no auth required, max 50 results with default limit 10, relevance sorting, and the important non-error empty-array behavior for zero matches. This is exactly the behavioral context an agent needs before calling.

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 tight sentences, front-loaded with identity and capabilities, then result/limit behavior, then routing. Every clause carries actionable information with no repetition of the tool name 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?

No output schema exists, and the description compensates by describing the return shape (array of up to 50 matches, sorted by relevance, empty array on no match) along with a next-step workflow, so an agent has everything needed to call and consume the result 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% (Chinese-language descriptions), so baseline is 3; the description adds real meaning beyond the schema by stating keyword accepts both Chinese and English and by summarizing the filter set and limit semantics in the agent's working language.

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 verb+resource ('Search in-stock products') plus the platform identity (Ucun Select, home-textile wholesale, Nantong China), and explicitly distinguishes itself from both siblings by naming get_stock for details and list_categories for an overview.

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?

Gives explicit when-to-use ('Use this to discover products') and routes the agent to the correct follow-up tool with the exact input needed ('call get_stock with a returned id'). No alternative or exclusion is left to inference.

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