Hunt Auto Group Texas: used car data

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so the safety profile is fully covered. The description adds genuinely useful behavioral context that the annotations do not: the advice is free and 'he does not sell cars', which shapes how the agent should frame the answer and prevents it from misrepresenting the contact as a sales channel. What it does not disclose is the shape of the returned contact options or whether the channel param filters the result set.

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 short sentences, front-loaded with the resource and channels, and the closing 'He does not sell cars' earns its place as a disambiguation cue. 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 simple one-optional-param read tool with full annotation coverage and no output schema, the description supplies enough for an agent to invoke it correctly and to frame the response. A brief note on what the return value contains (per-channel handles only, or all channels when unfiltered) 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 coverage is 100% and the single optional 'channel' parameter has an enum of email/whatsapp/form, so the schema already does the work. The description restates the same three channels but adds no semantics about filtering behavior — e.g. whether omitting channel returns all channels or a default one — so it sits at the baseline.

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 the concrete resource — a person's contact channels (WhatsApp, email, form) for free used-car advice — which is specific enough to distinguish it from the sibling tools (get_model, list_guides, etc.), which all deal with car data rather than contact routes. It only lacks an explicit verb form, but the resource is unambiguous.

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: an agent can infer it should call this when a user wants to reach Anthony Hunt, and the phrase 'free used-car advice' gives the qualifying context. There is no explicit when/when-not guidance and no mention of the sibling tools or what to do if a channel is unavailable.

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 value beyond that by disclosing what the payload contains (four data categories plus page links), which matters because there is no output schema.

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 scope ('one model across all years') followed by the payload contents. The telegraphic colon-list style is efficient, though the omitted verb makes the opening slightly clipped.

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 one-parameter read tool with no output schema, the description covers the essentials: scope and returned data categories. Residual gaps (pagination behavior of 'page links', exact response shape) are minor given the rich annotation set.

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 slug parameter is fully documented in-schema with an example, so the description need not repeat format details. It only implies the single-model scope, adding 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 states the resource ('one model') and enumerates the returned data (NHTSA recalls, complaints, overall stars, EPA MPG), and 'across all years' implicitly contrasts with the sibling get_model_year. It does not name the sibling explicitly, so the differentiation is inferable 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?

Usage is only implied: 'across all years' suggests this is the aggregate view versus a per-year lookup, but the description never names get_model_year or state when to prefer one over the other. No prerequisites or exclusions are given.

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 real value by disclosing which external datasets are merged into the response (NHTSA, EPA, Transport Canada), which matters since there is no output schema. It stops short of noting that data may be missing or stale for certain years/models.

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 the scope claim first and a semicolon-separated inventory of contents after. Every clause names actual data an agent would get, with no filler or restatement of the tool 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?

For a simple two-key read-only fetch with full schema coverage, no output schema, and complete annotations, the definition supplies what an agent needs to call it and to know what data comes back. The only real omission is how it relates to the sibling get_model and what happens when the year is unavailable.

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% for both required parameters, including the slug format example (toyota-tacoma) and the year pattern. The description adds no meaning beyond the schema for either parameter, which is the expected baseline when the schema already documents everything.

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 model year in detail') and enumerates the returned data domains (NHTSA recalls/complaints/crash stars, EPA engines and MPG, Transport Canada size/weight). An agent can tell this is a per-year detail lookup. It does not explicitly distinguish itself from the sibling get_model, which an agent must infer covers the model without a year.

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, when-not-to-use, or alternative routing. Usage is only implied by the enumerated contents: an agent infers it should call this when it needs recalls, crash ratings, or EPA specs for a particular year. The relationship to get_model and list_models is left entirely 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?

Annotations already declare readOnly, idempotent, non-destructive, closed-world behavior, so the safety profile is fully covered. The description adds one genuine behavioral fact beyond them: only 'published' guides are returned, implicitly excluding drafts. It says nothing about ordering, pagination semantics, or result shape.

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 hedging or boilerplate. The parenthetical topic list is slightly bulky but earns its place by scoping what content exists, which aids tool selection.

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 more burden than usual and never states what a result looks like (fields, ordering) or that results are paginated via next_cursor. For a simple read-only list tool that gap is modest, but it is a real omission.

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 five parameters (q, lang, limit, cursor, category) are already documented with enums and defaults. The description restates the three filter dimensions without adding semantics such as AND/OR combining rules or how cursor interacts with limit, 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?

Names the resource precisely (published guides, reviews, comparisons) and enumerates the topical scope (Texas title, tax, inspection, auctions, best years), so an agent immediately knows this is editorial content rather than model/vehicle data. It does not explicitly contrast itself with the get_model/list_models siblings, but the domain gap is self-evident from the noun.

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 phrase 'filtered by language, category or keywords' implies browsing/discovery usage and tells the agent which levers exist. There is no explicit guidance on when to prefer this over a sibling, no statement about prerequisites, and no exclusion cases — usage is only implied.

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, openWorldHint=false and destructiveHint=false, so safety is covered. The description adds genuinely new context: the dataset's geographic audience (Austin to San Antonio used-car buyers), the fields each record carries, and the cursor-based pagination model.

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 with no filler, and the resource scope plus returned fields are front-loaded ahead of the pagination note. The parenthetical audience detail is slightly tangential but still earns its place as scope context.

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, no-required-parameter read tool with no output schema, the description covers what is returned, how to page, and the data's scope. It leaves sort order and page-size behavior to the schema, which is acceptable given 100% schema coverage.

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 limit, cursor and category are already fully documented with defaults, bounds and enum values. The description only restates the cursor-pagination concept and does not add meaning (e.g. sort order or what a page contains) beyond the schema, 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 resource (car models the site has data for) and enumerates the fields returned (slug, category, years), so an agent can distinguish it from the singular get_model / get_model_year siblings. It stops short of explicitly naming those siblings as the alternative for single-record lookups.

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: 'paginate with next_cursor' tells the agent how to page and the schema's category param hints at filtering, but there is no explicit when-to-use guidance or when to prefer get_model instead.

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