klengnest

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

Annotations already declare read-only, idempotent, non-destructive, closed-world, so the safety profile is covered. The description adds real behavioral context beyond that: rankings exclude personal answers, a null size count means no source supplies one, and a decision with no products falls back to 'what to look for'. Since no output schema exists, this return-shape detail is doing needed work.

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 dense sentences with no filler; the payload contents come first and the edge-case semantics follow. Slightly scattershot — the size_plan clause interrupts the flow of the return-shape list — but nothing is wasteful.

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 no output schema, the description reasonably covers what comes back (look-for guidance, quantities, safety notes, ranked options) plus two important special cases. It omits any relationship to the sibling lookup tools, but for a single-key read it is nearly complete.

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?

Only one parameter and schema description coverage is 100% — the schema already states decision_key is sourced from klengnest_list_categories with an example value. The description adds nothing about the key's format or origin, 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?

The description enumerates what the decision object contains (what to look for, how many to buy, safety notes, ranked options), which is specific but never states the action or resource in verb+noun form — the verb 'Get' lives only in the title. It also never contrasts itself with klengnest_get_product or klengnest_search_products, so the boundary between a 'decision' and a 'product' is left to inference.

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?

No when-to-use guidance and no mention of any sibling alternative, even though klengnest_get_product and klengnest_search_products are obvious candidates for confusion. It only covers data edge cases (null count, decision with no products), not invocation context.

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 cover the safety profile (readOnly, idempotent, non-destructive, closed-world), so the bar is lower. The description adds real payload context, especially the non-obvious 'evidence_gaps' concept, which tells the agent it may receive warnings about unverified data. It omits pagination/response-shape limits, but no output schema exists so this partial disclosure is still valuable.

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 with no wasted words; it leads with scope and then enumerates contents, so the agent learns the payload before any 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?

With no output schema, the description carries the return-value burden and does so reasonably by naming the main sections of the record. It is complete enough to call correctly, though it says nothing about what happens for an unknown or malformed product_id.

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?

Only one parameter and schema description coverage is 100%; the schema already gives the format and an example ('cybex-cloud-t'). The description adds no further syntax or sourcing guidance, so 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+resource ('everything recorded about one product') and enumerates the payload categories: prices with provenance, availability by market, reviews and tests, evidence_gaps. It is clearly distinct from siblings like search_products or list_categories, though it never names them explicitly.

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?

No when-to-use, when-not-to-use, or alternative is given. The agent can infer this is the single-product detail fetch (versus bulk search), but that inference is left entirely to the reader; nothing tells it when this tool is preferable to klengnest_search_products.

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 the safe read-only, idempotent, closed-world profile, so the safety burden is covered. The description adds genuinely new behavioral context by enumerating the four kind values (product_choice, commodity, fit_dependent, clinician_led) and what each means, which is information an agent cannot get from the empty schema or 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?

Three compact sentences, front-loaded with what is returned, then the routing purpose, then the enum legend. No filler; every sentence adds distinct 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?

With no output schema, the description must carry the return-value burden, and it does reasonably well by describing categories containing decisions, the path-shaped identifier format, and the kind taxonomy. It stops short of describing ordering or completeness guarantees, but an agent has enough to call it and use the result.

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, so the 4 baseline applies. The description goes slightly beyond by explaining the kind field that the returned items carry, but it is describing a response field rather than an input.

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 and resource (list every category and its decisions) and immediately differentiates from siblings by positioning itself as the entry point for finding a decision_key. The example path "01-stroller/stroller" makes the returned structure concrete.

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?

"Start here to find a decision_key for the other tools" gives explicit routing guidance and names the downstream consumers (get_decision, get_product, search_products). It does not state any exclusion or when not to call it, but the intended entry-point role is unambiguous.

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 annotations already declare readOnly, idempotent, non-destructive, and closed-world behavior, so the safety profile is covered. The description adds a non-obvious behavioral detail: products with no known price are included unless max_price_eur is set. It does not cover return format or pagination behavior, but with annotations in place the bar is lower.

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 tightly written sentences with no wasted words and the core search scope front-loaded. Every phrase adds either the resource, the optional filters, or a meaningful behavioral caveat.

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 five-parameter search tool with no output schema and rich safety annotations, the description covers the key search scope and the non-obvious price-filter behavior. Pagination defaults and limits are documented in the schema, so the omission of those details is acceptable, though return shape and sorting are not addressed.

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 40%, so the description must compensate. It clarifies that query matches name or brand and that max_price_eur controls inclusion of price-less products, but it leaves limit and offset undocumented anywhere. This is partial compensation, fitting a baseline 3.

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 ('Find') and resource ('products'), along with optional filters ('by name or brand, optionally within a category or under a price'). It clearly distinguishes the tool from a get-single-product or list-categories tool, though it does not explicitly name 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 through the search-by-name/brand framing and optional filters, giving an agent a reasonable sense of when to use it. However, it offers no explicit when-not guidance or alternative-tool routing against siblings like get_product or list_categories.

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