list_generations
Generations/facelifts of a model, with production years.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| locale | No | ||
| model_id | Yes |
Generations/facelifts of a model, with production years.
| Name | Required | Description | Default |
|---|---|---|---|
| locale | No | ||
| model_id | Yes |
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?
With no annotations available, the description carries the full burden of behavioral disclosure. It does not state whether the operation is read-only, requires authentication, or what the response format looks like. The minimal description only hints at output (production years) but lacks critical behavioral details.
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 a single, concise phrase with no wasted words. However, it is too brief and lacks structure, failing to provide necessary details about parameters or output. It is under-specified rather than optimally concise.
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 no output schema and simple parameters, the description is incomplete. It does not describe the return format (e.g., list of objects with fields), error conditions, or ordering. The agent has no way to know what to expect after invocation.
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 0%, and the description does not clarify the purpose or expected values of the model_id or locale parameters. The locale parameter has an enum but is not mentioned, and model_id is not explained beyond the vague reference 'of a model'. The agent gets no help understanding how to fill these parameters.
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 indicates that the tool returns generations/facelifts of a model with production years, which distinguishes it from sibling tools like filter_cars or get_specs. However, it lacks an explicit action verb like 'list' to match the tool name.
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 guidance is provided on when to use this tool versus alternatives such as search_cars or compare_variants. There is no mention of prerequisites or context, leaving the agent to infer usage solely from the name.
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.