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Analyze used-car listing

analyze_listing

Start a BuyerAware pre-purchase verdict on ONE used vehicle. Runs a mechanic-grade read: ranked failure points with $ exposure, recalls, seller-claim fact-check, fair price range, adjusted true cost, risk score 1-10, BUY / BUY_WITH_CONDITIONS / WALK_AWAY, a word-for-word negotiation script, and lower-risk 'Tank' alternatives. Supply whichever of listing_url, listing_text or vin you have — at least one is required, and more context sharpens the verdict. Takes ~1-2 minutes: returns a job_id, then poll get_verdict. Costs 1 credit. For 'what should I buy instead' use tank_alternatives, which is free.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
vinNo17-character VIN, if the listing shows one
locationNobuyer city/state, for pricing and inspection context
listing_urlNolink to the listing — Marketplace, Craigslist, AutoTrader, a dealer page
listing_textNothe listing copy pasted as-is, when there is no shareable URL
buyer_contextNowhat the buyer needs it for, budget, and any specific concerns

TDQS

A5/5.0
Behavior5/5

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

Annotations already signal non-read-only, non-idempotent, non-destructive, open-world behavior, and the description adds important context beyond those: it takes 1–2 minutes, returns a job_id to be polled, and costs 1 credit. This gives the agent a clear model of the side effects and latency without contradicting the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but every sentence earns its place: verdict purpose, output summary, input guidance, async behavior, cost, and sibling routing. It is front-loaded with the primary purpose and ends with the alternative-tool pointer, making it easy to scan.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description compensates by naming the return flow (job_id, then poll get_verdict), the main verdict fields, the credit cost, and the free alternative. An agent has enough to invoke the tool correctly and understand what will happen next.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so a baseline of 3 is warranted, but the description adds crucial semantics: it groups listing_url, listing_text, and vin as alternative sources, states that at least one is required, and explains that providing more context sharpens the verdict. This materially helps an agent choose and combine parameters beyond what the schema states.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Begins with a specific action and resource: 'Start a BuyerAware pre-purchase verdict on ONE used vehicle.' It enumerates concrete outputs (risk score, verdict, negotiation script, alternatives) and explicitly distinguishes itself from tank_alternatives and get_verdict, so an agent can separate it from sibling tools without inspecting their schemas.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit usage conditions: use it for a single used-vehicle pre-purchase verdict; supply at least one of listing_url, listing_text, or vin; more context improves accuracy. It also names the alternative tool for a different intent ('what should I buy instead' → tank_alternatives) and the follow-up step (poll get_verdict).

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

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TDQS

A4.4/5.0
Disambiguation5/5

Each tool targets a distinct step: starting a paid async analysis, polling for its result, creating the API key, and two different free lookups (recommendations vs. single-platform lookup). The descriptions explicitly cross-reference when to use each, leaving little room for misselection.

Naming Consistency3/5

The three action tools (analyze_listing, create_trial_key, get_verdict) follow a clear verb_noun pattern, but tank_alternatives and tank_lookup break it by leading with the noun 'tank'. The tank_ prefix is recognizable, but the set mixes verb-first and noun-first naming.

Tool Count5/5

Five tools is a tight, appropriate scope for a pre-purchase vehicle-verdict service: one paid analysis, one polling endpoint, one API-key setup, and two free instant database queries. Each tool earns its place and none feels redundant.

Completeness5/5

The core lifecycle is covered: create_trial_key enables access, analyze_listing starts a job, get_verdict retrieves its outcome, and tank_alternatives/tank_lookup cover adjacent free research workflows. There are no obvious dead ends or missing operations for the stated purpose.

Resources