get_safety_ratingsGet NHTSA 5-Star Safety Ratings (NCAP crash-test results) for a vehicle — overall, frontal, side, and rollover star ratings. Use for "crash test rating for a 2021 Honda Civic", "how safe is X". Pass make + model + model_year.
| Name | Required | Description | Default |
|---|---|---|---|
| make | Yes | Vehicle make (e.g., "Honda") | |
| model | Yes | Vehicle model (e.g., "Civic") | |
| model_year | Yes | Model year (e.g., 2021) |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "make": "Honda",
+ "model": "Civic",
+ "model_year": 2021
+ },
+ {
+ "make": "Volvo",
+ "model": "XC90",
+ "model_year": 2023
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds that the tool returns overall/frontal/side/rollover star ratings and uses NHTSA NCAP data, which is useful context about the return content. However, it does not disclose potential limitations (e.g., missing ratings for some vehicles) or additional behavioral details beyond what annotations cover.
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 long, front-loaded with the core purpose, and every word earns its place. It includes a clear scalar phrase and two example queries, with 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?
The description covers the tool's return type (star ratings across multiple dimensions), the data source (NHTSA NCAP), and required parameters. Since there is no output schema, mentioning the rating categories provides sufficient context for a simple lookup tool. It does not address error handling or empty results, but that is not essential for this straightforward 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?
Schema description coverage is 100% and each parameter is documented with examples. The description merely restates 'Pass make + model + model_year' without adding new meaning beyond the schema, so the baseline of 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?
The description uses a specific verb ('Get') and identifies a distinct resource (NHTSA 5-Star Safety Ratings / NCAP crash-test results) with clear scope (overall, frontal, side, rollover). It also provides example queries, distinguishing it from sibling tools like get_recalls or get_complaints.
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 states when to use the tool ('Use for “crash test rating for a 2021 Honda Civic”, “how safe is X”') and what parameters to pass ('Pass make + model + model_year'). It does not mention alternatives or exclusions, but the positive guidance is concrete and actionable.
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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