get_makesGet all vehicle brands for a model year. Returns make names and IDs. E.g., year '2023'.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| count | Yes | Total count of vehicle makes | |
| makes | Yes | List of vehicle makes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Output schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "count": {
+ "description": "Total count of vehicle makes",
+ "type": "number"
+ },
+ "makes": {
+ "description": "List of vehicle makes",
+ "items": {
+ "properties": {
+ "id": {
+ "description": "Make ID",
+ "type": [
+ "number",
+ "null"
+ ]
+ },
+ "name": {
+ "description": "Make name",
+ "type": [
+ "string",
+ "null"
+ ]
+ }
+ },
+ "required": [
+ "id",
+ "name"
+ ],
+ "type": "object"
+ },
+ "type": "array"
+ }
+ },
+ "required": [
+ "count",
+ "makes"
+ ],
+ "type": "object"
+}Input schema / examplesAdded value: +[
+ {}
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds that it returns make names and IDs, and implies filtering by year, which provides useful behavioral context beyond the annotations. The only issue is the ambiguous year reference given the empty schema.
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 three short sentences, front-loaded with the primary action, followed by return type and an example. Every sentence adds value, and there is no wasted prose.
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 core function and return content are covered, and annotations handle the safety profile. However, the discrepancy between the described year input and the empty schema leaves a significant gap, making the description incomplete for a tool that appears to require a model year argument.
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 input schema has zero properties, yet the description mentions a 'model year' and provides the example 'year 2023' without specifying a parameter name, type, or format. This introduces confusion rather than clarifying parameter semantics. With no schema parameters, the description should either confirm no parameters or properly document the year input.
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 the tool retrieves all vehicle brands (makes) for a model year. It includes a specific example and distinguishes itself from sibling tools like get_models by focusing on 'makes' rather than other entities.
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 context 'for a model year' implies when to use it: when a list of makes for a given year is needed. It does not explicitly mention alternatives or exclusions, but the usage context is clear enough for a simple tool.
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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