search_vehicle_complaintsSearch NHTSA consumer complaints about vehicles by make, model, and/or component. Returns incident details including injuries, deaths, crashes, fires, and complaint summary.
| Name | Required | Description | Default |
|---|---|---|---|
| make | No | Vehicle make (e.g., Ford, Toyota) | |
| model | No | Vehicle model (e.g., F-150, Camry) | |
| component | No | Vehicle component (e.g., STEERING, BRAKES, AIR BAGS) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true. The description adds that the return includes injuries, deaths, crashes, fires, and complaint summaries—useful but not revealing new behavioral traits beyond the annotations. No discussion of rate limits, authentication, or result limitations.
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 concisely convey the tool's purpose and return content. Front-loaded with the action and resource, no wasted words.
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?
The description mentions the key return fields (injuries, deaths, crashes, fires, complaint summary), compensating for the lack of an output schema. However, it omits details on pagination, result limits, or how component values should be specified, which would be helpful for a search 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?
The schema covers 100% of parameters with descriptions. The description rephrases 'by make, model, and/or component', matching the schema. It adds no additional meaning beyond what the schema provides, warranting the baseline score.
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 specifies the verb 'Search', the resource 'NHTSA consumer complaints about vehicles', and the filtering parameters (make, model, component). It distinguishes from sibling tools like 'search_vehicle_recalls' and 'search_consumer_complaints' by focusing on NHTSA vehicle complaints and listing return details.
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 implies usage for searching NHTSA complaints, but provides no explicit guidance on when to use this tool versus siblings (e.g., 'search_vehicle_recalls', 'search_consumer_complaints'). No alternatives or exclusions are mentioned.
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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Each tool has a clear, specific purpose with detailed descriptions that differentiate them. Prefix patterns like get_district_, search_, analyze_, get_, etc., help an agent easily identify the correct tool for a task.
All tool names use a consistent verb_noun or verb_noun_noun pattern with underscores. The naming convention is uniform across the entire set, with no mixing of styles or ambiguous verbs.
With 47 tools, the count is high but justified by the broad scope of civic data analysis. While some agents might find the sheer number overwhelming, the tools are organized into clear categories (district profiles, searches, analyses) that make navigation feasible.
The toolset covers an impressively wide range of domains: legislation, representatives, districts, voting, committees, campaign finance, lobbying, federal spending, regulations, environment, energy, healthcare, housing, disaster, banking, consumer complaints, crime, vehicles, and more. There are no obvious missing operations for a civic data platform.