PadelTrue padel racket data

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

Annotations already declare readOnly, idempotent, non-destructive and closed-world. The description adds real value beyond them by specifying what is returned (both records, sourced differences, missing information, a comparison URL) and by explicitly prohibiting an inferred winner or medical-suitability judgement, which shapes how an agent should present results.

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 compact sentences: the first front-loads what the tool does and its key invariant, the second states the output and the prohibitions. No filler, no 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?

With no output schema, the description carries the burden of describing returns, and it does so reasonably (both records, sourced differences, missing information, URL). What remains unspecified is error behavior for an unknown identifier and what 'missing information' concretely contains.

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 two parameters are already documented. The description adds limited meaning beyond the schema — mainly that both identifiers must belong to the same dataset and be compared under one rating model, which implies a is not a preference and the pair must be compatible.

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: compare two exact catalogue identifiers using the same dataset and rating model. The word 'exact' implicitly separates it from search_rackets (fuzzy lookup) and get_racket (single record), though no sibling is named 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?

Usage is only implied: you supply two exact identifiers, which presupposes both models are already known (so search_rackets would be used first). The closing sentence is an output constraint, not guidance on when to choose this tool over get_racket called twice.

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 read-only, idempotent, non-destructive behavior, so the safety profile is covered. The description adds real value beyond that by disclosing the payload contents, including spec source URLs/quotations, calculated ratings, a model version, and 'missing rating inputs' — warning the agent that partial data is possible. It does not cover error behavior for an unknown slug or rate limits.

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 purpose sentence leads with the verb and resource, then enumerates the returned fields; the usage sentence is short and front-loaded. The long enumeration of payload fields is dense but each clause earns its place by telling the agent what to expect.

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 burden of describing return values and does so thoroughly, including the product-page URL and the possibility of missing rating inputs. Combined with annotations covering the safety profile and the schema documenting the slug, an agent has everything needed 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 description coverage is 100% and the single required param already carries a concrete format example ('nox-at10-luxury-genius-18k-alum-2026'). The description only restates that the identifier is 'returned' from search_rackets, adding traceability but no syntax beyond 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 specific verb ('Retrieve') and resource ('one exact model'), and the qualifier 'one exact' implicitly distinguishes it from its sibling search_rackets, which returns many. An agent can tell the two apart without opening either 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?

It gives explicit context, 'Use this to verify a candidate before recommending it,' and identifies the slug as coming from search_rackets, which implies the search-then-get workflow. It stops short of naming compare_rackets as an alternative or stating exclusions, so it is clear but not fully routing.

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 readOnly, idempotent, non-destructive, and closed-world, so the safety profile is covered. The description adds genuine behavioral context beyond that: no exchange rate is applied, results are limited to this catalogue, and prices do not establish stock or delivery eligibility.

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 the action and scope, followed by what is returned and the caveats. Every sentence carries information; there is no filler or repetition of the title.

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 13-parameter tool with no output schema, the description compensates by enumerating the returned fields (identifiers, URLs, price and check date, ratings, coverage, ordering rule) and by flagging the semantics/scope limits. Nothing critical for correct invocation appears to be missing.

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?

With 85% schema description coverage, the schema already documents most parameters including sort ordering, limit default/max, and currency behavior. The description's 'explicit filters' and mention of 'the applied ordering rule' add only marginal meaning beyond what the schema provides, 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 ('Search the PadelTrue catalogue') and clarifies scope with 'by model name and explicit filters' plus 'Results cover this catalogue, not the entire market.' It does not explicitly distinguish itself from compare_rackets or get_racket, but the search-vs-retrieve-vs-compare distinction is readily inferable.

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?

Usage is implied rather than stated: the agent can infer this is the discovery tool and that get_racket/compare_rackets serve narrower purposes, but no explicit when-to-use or when-not-to-use guidance is given. The scope caveats (catalogue-only, prices don't imply stock) are helpful context but not routing guidance.

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