Structured catalog filter: fuel type, body type, drive layout, power/price range, model year, EV-only.
| Name | Required | Description | Default |
|---|---|---|---|
| ev | No | ||
| body | No | body_slug, e.g. suv, sedan, hatchback | |
| fuel | No | e.g. petrol, diesel, electric, hybrid | |
| year | No | ||
| drive | No | substring match, e.g. 'front', 'rear', 'all' | |
| limit | No | ||
| locale | No | ||
| power_max | No | ||
| power_min | No | ||
| price_max | No |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It does not disclose behavioral traits such as read-only nature, pagination via 'limit' parameter, locale handling, or what the output contains. The description only lists filterable criteria.
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?
A single sentence that is concise and front-loaded with 'Structured catalog filter'. No unnecessary words, every part contributes to the purpose.
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 complexity (10 parameters, no output schema, no annotations), the description is too brief. It lacks details on return values, filtering logic (AND/OR), pagination behavior, and error handling. The sibling 'search_cars' likely requires differentiation.
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 30%. The description adds meaning by grouping parameters into categories (e.g., 'power/price range', 'model year', 'EV-only'), which helps for some params. However, it does not clarify all 10 parameters individually, and some like 'locale' and 'limit' are omitted.
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 it's a catalog filter and lists the filterable attributes (fuel type, body type, etc.), indicating the tool's purpose. However, it does not differentiate from the sibling 'search_cars', which likely has similar functionality.
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?
No explicit guidance on when to use this tool versus alternatives like 'search_cars' or 'compare_variants'. The description implies it's for structured filtering but provides no context on when it's appropriate or not.
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, distinct purpose: search, filter, get specs, get images, compare, and list generations. No overlap between their functionalities.
All tool names follow a consistent verb_noun pattern (e.g., search_cars, get_specs, list_generations), with no mixed conventions.
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.
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.
