Elétrico Leve

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

With no annotations, the description carries the full disclosure burden. It does disclose the key modeling assumption (a measured 24 Wh/km consumption baseline) that drives the result, which is useful context, but it never states that this is a pure, side-effect-free calculation, nor how carga_extra_kg perturbs the estimate.

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 states the computation and its method with zero filler. 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?

No output schema exists, so the description correctly names both returned quantities and their units. It is nearly complete for a 4-parameter calculator; the only omission is any word on how extra weight enters the estimate.

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 all four parameters including the defaults (consumption 24, extra weight 0) are already documented in structured form. The description only restates the voltage/Ah baseline and adds no format or units detail 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 pairs a specific verb (calculate) with specific outputs (usable battery energy in Wh and realistic range in km) and the inputs they derive from (voltage and Ah). It is unambiguous and clearly distinct in kind from the model-listing siblings, though it never references 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?

Usage is only implied by the input list: an agent can infer this is a computation tool, but there is no statement of when to call it versus the vehicle/model tools, and no preconditions or exclusions. Adequate but leaves routing 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 behavioral burden and does disclose the substantive logic: the four output categories and whether each requires a plate (plus ACC or CNH A). What is missing is anything about error handling or how borderline/ambiguous inputs are resolved, so it is strong but not exhaustive.

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 sentences, front-loaded with the purpose and regulation before the category/plate mapping. Dense but every element (verb, regulation, categories, plate consequence) earns its place; only the long final enumeration slightly reduces readability.

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 tool with no output schema and no annotations, the description conveys what is returned (a category and a plate requirement) and the legal basis. It stops short of describing the response shape or handling of ambiguous inputs, but an agent has enough 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%, so all seven parameters are already documented in the schema, including which fields are 'needed for autopropelido'. The description adds no additional parameter meaning or format guidance beyond that, 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 (classify) and resource (Brazilian light electric vehicle) plus the exact legal instrument, Resolução Contran 996/2023 art. 2. The category enumeration with plate requirements makes it unmistakably distinct from calculate_range, compare_models, list_models and search_models.

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 by the domain: this is the only regulatory classifier among model search/compare/range siblings, so an agent can infer when it applies. However there is no explicit statement of when to pick this over the sibling tools or what inputs are needed to qualify a vehicle.

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. It implies a read-only comparison ('side by side: specs, legal category and price') and caps input at five, but says nothing about permissions, whether models in different legal categories can mix, or error behavior for invalid ids.

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 efficient sentence that front-loads the verb and enumerates exactly what is compared. No filler, no redundancy with 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 simple, single-parameter read tool with no output schema, the description covers scope, bound, and the compared attributes adequately. A note on id validity handling or the comparison output shape would close the remaining gap.

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% with a single parameter, so the schema already documents that ids are model ids from list_models bounded 2-5. The description restates the same constraint ('two to five') without adding format or validation meaning. 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 (compare) and resource (catalogue models) plus the comparison dimensions: specs, legal category, and price. It distinguishes the outcome from the list/search siblings, though it does not name them explicitly the way a 5-scoring definition would.

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 'two to five' bound and the '(from list_models)' hint imply you must first obtain ids via a sibling tool, which is useful context. However, there is no explicit when-to-use vs when-not statement or routing guidance against search_models or list_models for alternative scenarios.

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 exist, so the description carries full burden, but it does disclose the shape of returned records (power, speed, battery, range, price, legal status), which is real value given there is no output schema. It stops short of covering ordering, pagination, result limits, or auth requirements for a catalogue that could be large.

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 dense sentence with the verb and resource front-loaded and no filler. The vehicle-type enumeration is slightly redundant since tipo already lists them, but it remains efficient overall.

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 two-optional-parameter read tool with full schema coverage, the description adequately conveys what is returned. It is incomplete on placement within the sibling set (search_models/compare_models), which an agent needs to route 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%: tipo documents its filter values and so_sem_placa explains the no-plate filter. The description adds nothing 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 (List) and resource (catalogue of e-bikes, scooters, foldables, tricycles) and enumerates the returned specs, so the agent knows exactly what the tool produces. It does not, however, distinguish itself from the sibling search_models, leaving the list-vs-search boundary ambiguous.

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 is offered and no alternative is named. With search_models, compare_models and classify_vehicle as siblings, the description gives the agent no signal for choosing list_models over search_models.

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. It does add one useful behavioral fact — results are returned as "best matches sorted by relevance" — but it omits result count/pagination, whether an empty filter set returns everything, and any performance or rate-limit characteristics. For a read-only search this is modest but incomplete disclosure.

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 tight sentences with no filler; the searchable dimensions are front-loaded and the return behavior is stated second. Nothing could be cut without losing meaning.

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 7-parameter, zero-required, no-annotation, no-output-schema tool, the description should clarify what an unfiltered call returns (which overlaps with list_models) and roughly what comes back. It gives the relevance-sort fact but leaves the zero-filter case and result shape unexplained.

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 71% (potencia_min_w and bateria_removivel are undocumented), so the schema mostly does the work. The description maps plain-language concepts (budget, power, range, load capacity, battery type) onto those filters, adding mild disambiguation, but adds no syntax, units, defaults, or semantics for the two undocumented parameters.

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 gives a specific verb ("Search") and resource ("the catalogue") and enumerates the filterable dimensions (budget, power, range, load capacity, battery type), so the agent knows exactly what it does. It does not, however, distinguish itself from the sibling list_models, which presumably also returns models.

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: the mention of filters suggests this is the tool to use when narrowing by criteria, but there is no explicit when-to-use/when-not guidance and no reference to alternatives like list_models or compare_models. With zero required parameters, the description also never says what happens when called with no filters.

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