list_makes_modelsList all supported car makes and their models (slugs, EV flag, price multiplier). Use the make_slug values with get_repair_cost_estimate and list_repair_types.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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?
Annotations already mark the tool as read-only and non-destructive. The description adds useful behavioral context beyond the annotations by specifying the contents of the catalog (slugs, EV flag, price multiplier) and confirming it returns the full supported set rather than a filtered subset.
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?
Two sentences with no redundant wording. The first sentence states scope and return fields; the second provides actionable downstream guidance. Every clause earns its place.
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?
For a parameterless read-only list tool with no output schema, the description is complete: it states the exhaustive scope, lists the relevant output fields, and explains how to consume the results with sibling tools. Nothing needed for correct invocation is missing.
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 schema has zero parameters, so there is nothing for the description to explain about parameter meaning. The description still adds value by clarifying what the returned data contains and how the make_slug values should be used downstream.
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 ('List') with a clearly bounded resource ('all supported car makes and their models') and names the exact fields returned. This distinguishes it from other list-style siblings like list_repair_types and list_repair_guides by focusing on the vehicle make/model catalog.
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 tells the agent exactly what to do with the output: use make_slug values with get_repair_cost_estimate and list_repair_types. For a parameterless catalog tool this is the primary usage, so no alternative-routing guidance is needed.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct resource or action: VIN decoding, vehicle valuation, repair cost estimates, editorial guides, and discovery lists are clearly separated. get_repair_cost_estimate and get_repair_cost_guide are slightly overlapping since both deal with repair pricing, but their descriptions clarify the estimate-vs-editorial-guide distinction.
All tool names follow a consistent verb_noun snake_case pattern: decode_vin, estimate_vehicle_value, get_repair_cost_estimate, get_repair_cost_guide, list_makes_models, list_repair_guides, list_repair_types. The naming is predictable and groups related operations clearly.
Seven tools is well-scoped for a car repair cost estimation server. Each tool earns its place: three discovery tools, two repair-cost tools, one VIN decoder, and one vehicle value estimator cover the domain without unnecessary bloat.
The tool surface covers the core workflows: discovering supported makes/models and repair types, decoding VINs, estimating vehicle value, getting repair cost estimates, and retrieving detailed editorial guides. There are no obvious dead ends, and the domain is read-only so CRUD-style gaps do not apply.