get_images
Image URLs (own CDN, no attribution burden) for a vehicle variant.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| variant_id | Yes |
cars-data.com — Car Specs API
Schema Changelog
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
- First observed
TDQS
B3.4/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions a behavioral trait (no attribution burden) and CDN source, but lacks details on pagination, URL format, or rate limits. Adequate but not comprehensive.
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?
Single sentence, front-loaded with key information. No unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple input (one parameter) and no output schema, the description is moderately complete. Missing details like number of images, URL format, and error cases, but adequate for a basic image retrieval tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so description must add meaning. It implies 'variant_id' identifies a vehicle variant, but does not explain what a variant is or how to obtain the ID. Minimal value added beyond schema.
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?
The description clearly states the tool returns image URLs for a vehicle variant, with a specific benefit (own CDN, no attribution). It distinguishes from siblings like get_specs or search_cars by focusing on images.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives like compare_variants or search_cars. The description does not provide context for usage or exclusions.
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
A3.5/5.0
Disambiguation5/5
Each tool has a clear, distinct purpose: search, filter, get specs, get images, compare, and list generations. No overlap between their functionalities.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern (e.g., search_cars, get_specs, list_generations), with no mixed conventions.
Tool Count5/5
With 6 tools covering all core functionalities of a car specs API (search, filter, specs, images, comparison, generations), the count is well-scoped and not excessive.
Completeness4/5
The tools cover the main use cases for a read-only car specs API. The only minor gap is the lack of a dedicated tool to list all brands or models, but the search tool can handle that need.