RevvedUpCars True Cost Index

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

Annotations already cover read-only, idempotent and closed-world traits, so the description's main value-add is the tie threshold ('differences under $10 a month are reported as a tie'), a real behavioral rule not present in any structured field. It also discloses the reported outputs, though error behavior for invalid slugs is unstated.

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 sentence with no filler, front-loading the comparison verb and resource before listing the returned facts and the tie 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?

With no output schema, the description does the work of describing the return contents (cheaper car, monthly/yearly delta, largest contributing cost line), which is a substantial and adequate disclosure. Only the tie-threshold caveat is covered; input failure handling and cost-line enumeration are not, so it falls just short of 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?

Schema description coverage is 100% and both parameters are documented as 'first/second car slug' with a slug pattern, so the schema carries parameter meaning. The description adds only that two cars are compared, which is already implied by the required 'a'/'b' pair.

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 ('Compare two cars' true monthly cost of ownership') and enumerates the exact outputs: which is cheaper, gap per month/year, and the dominant cost line. An agent can distinguish this from the single-car sibling get_true_cost purely from the description.

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-car comparison context is clearly implied, but the description never says when to prefer this over get_true_cost (single car) or cost_index, and gives no exclusions or prerequisites. Usage is inferable rather than 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?

Annotations already declare readOnlyHint, idempotentHint, non-destructive, and closed-world, so the safety profile is covered. The description adds the ordering guarantee and the returned aggregate figures, which is useful context beyond the annotations, but nothing about pagination or how the ranking is computed.

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 sentence with no waste, front-loading the core action and the sort order before the output detail. Slight redundancy from repeating 'segment', but overall tight and well-structured.

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-parameter, zero-required read tool with full schema coverage and read-only annotations, the description is largely sufficient and even compensates for the absent output schema by naming the returned figures (cheapest, median, most expensive). Only the relationship to sibling tools is left unstated.

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 both parameters (limit and the segment enum with its 'under-700' explanation) are fully documented in the schema. The description adds no parameter-level meaning beyond that, which is the expected baseline when the schema does the heavy lifting.

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 and resource ('Rank a segment of the catalogue by true monthly cost of ownership'), the sort order ('cheapest first'), and the aggregate output. It does not explicitly distinguish itself from siblings like compare_true_cost or get_true_cost, but the segment-ranking framing is distinct enough to infer.

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: an agent can infer this is the tool for ranking a whole segment rather than pricing an individual car. However, no alternative or 'when not to use' condition is stated, so the agent must infer the boundary with compare_true_cost and get_true_cost.

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 and closed-world, so the safety profile is covered. The description adds genuinely useful behavior beyond them: conjunctive token matching ('Every word must match') and the result ordering ('Newest model years first'), both of which affect how an agent forms and interprets queries.

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, front-loaded with the core action, and each clause carries non-redundant information (matching rule, return fields, sort order). 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 two-parameter read-only search with full schema coverage and no output schema, the description supplies what is needed: matching semantics, ordering, and the returned fields. It could be complete at 5 with a note on empty-result behavior or the limit interaction, but nothing essential is 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?

Schema coverage is 100%, so the baseline is 3. The description still adds meaning beyond the schema by clarifying the query semantics — all tokens must match — which is not expressible in a plain string schema and directly affects how the "toyota rav4" example behaves.

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 and resource ('Search the priced catalogue') and states the matching dimensions (make, model, trim, year) plus the return payload ('slug and true monthly cost'). It is clear without needing the schema, though it does not explicitly differentiate itself from siblings like get_true_cost or cost_index.

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?

There is no explicit when-to-use guidance and no mention of the sibling tools (compare_true_cost, cost_index, get_true_cost), so an agent must infer that this is the entry-point search. The matching constraint ('Every word must match') is a usage rule 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.

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

Annotations already declare readOnly, idempotent, non-destructive, and closed-world, so safety is covered. The description adds genuine behavioral context beyond that: it discloses that estimates may be either the car's own figure or a segment average, and that a rank against comparable cars is returned — a meaningful caveat about data provenance.

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 that front-loads the core purpose before listing payload contents. Every clause contributes, though the enumerated list makes it read as a run-on rather than cleanly segmented structure.

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 well, listing components, totals, the segment-average caveat, and the rank. It stops short of clarifying units/currency or what the comparison set for rank is, minor gaps for a cost-modeling tool.

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?

There is a single parameter with 100% schema description coverage, and the schema already documents slug with a format pattern and a concrete example. The description adds nothing about slug, so the baseline of 3 is appropriate.

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 resource and scope: "One car's true monthly cost of ownership," and enumerates the cost components (loan, insurance, maintenance, fuel). The phrase "one car's" implicitly separates it from compare_true_cost, but no sibling is named explicitly, so differentiation is inferred rather than stated.

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?

There is no when-to-use guidance, no prerequisites, and no mention of the adjacent tools compare_true_cost or cost_index. The singular-car scope weakly implies a single-car lookup, but an agent gets no explicit condition for choosing this tool over its siblings.

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