ui_select_vehicleApp-only: record a vehicle selection from the results widget. Not for model use — hosts filter via _meta.ui.visibility.
| Name | Required | Description | Default |
|---|---|---|---|
| url | No | ||
| vin | Yes | 17-character Vehicle Identification Number | |
| make | No | ||
| name | No | ||
| year | No | ||
| model | No |
| Name | Required | Description | Default |
|---|---|---|---|
| action | Yes | ||
| message | Yes | ||
| selected | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint, idempotentHint, and destructiveHint. The description adds that it is app-only and not for model use, which are behavioral constraints. No contradiction with annotations. The term 'record' might imply a write, but annotations suggest it is read-only; context clarifies it as a selection event, not a mutation.
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 description is two sentences with zero wasted words. It front-loads the key purpose and usage constraint, making it easy for an agent to quickly understand and decide on invocation.
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?
Given the tool has 6 parameters (1 required) and an output schema, the description is minimal but sufficient for a simple recording action. It explains the scope (app-only) and purpose. However, the lack of parameter elaboration may require the agent to rely heavily on parameter names, but most are self-explanatory (make, model, year).
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 only 17% (only vin has a description). The description does not add meaning for other parameters (url, make, name, year, model). With low coverage, the description should compensate, but it does not elaborate on parameter usage or relationships, leaving ambiguity for the agent.
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 clearly states the tool's purpose: 'record a vehicle selection from the results widget'. It specifies the resource (vehicle selection) and action (record), and distinguishes it from model use, making it clear among siblings.
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 description explicitly restricts usage to 'App-only' and warns 'Not for model use', providing clear when-to-use and when-not-to-use guidance. It also mentions filtering via '_meta.ui.visibility' for hosts, adding context for proper invocation.
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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Every tool has a clearly distinct purpose. Tools like compare_market, compare_models, and compare_vehicles target different scopes (segment, model, VIN), while search_vehicles and filter_vehicles serve different query types. No apparent overlap.
All tool names follow a consistent verb_noun pattern (e.g., check_recalls, compare_market, get_vehicle, list_facets). No mixing of naming conventions, making the set predictable and easy to navigate.
With 26 tools, the count is on the higher side but appropriate for a comprehensive automotive platform. Each tool covers a distinct aspect of car shopping and research, and there is no obvious redundancy.
The tool surface covers the full lifecycle of automotive research and shopping: recalls, comparisons, dealerships, vehicle search and details, history, deal scoring, market trends, safety ratings, rankings, and lead submission. Only minor gaps like trade-in valuation exist.