get_repair_cost_estimateEstimate the cost of a car repair job (parts + labor, USD, US national averages 2026) by make and repair type. Returns low/high ranges for parts, labor, and total. Use list_makes_models and list_repair_types to discover valid slugs.
| Name | Required | Description | Default |
|---|---|---|---|
| make_slug | Yes | Car make slug, e.g. "ford", "bmw", "tesla" | |
| part_slug | Yes | Repair type slug, e.g. "brake-pads", "head-gasket", "windshield" |
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 declare readOnlyHint=true and destructiveHint=false, covering the safety profile. The description adds useful behavioral context: it returns low/high ranges for parts, labor, and total, and specifies the data basis as US national averages for 2026. This goes beyond the annotations without contradicting them.
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 compact: two sentences that front-load the core purpose, then state the return format, then provide prerequisite lookup guidance. Every sentence earns its place with no repetition or filler.
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 description explains what the tool returns, the currency and region basis, and how to obtain valid parameters, which is sufficient for a read-only estimate tool with no output schema. It stops short of covering possible error behavior or clarifying the relationship to get_repair_cost_guide, but these are minor gaps.
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 100%, and both make_slug and part_slug are documented with examples. The description reinforces that part_slug is a repair type and tells the agent to discover valid slugs, but adds little semantic meaning beyond what the schema already provides. Baseline 3 is appropriate.
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 estimates car repair costs by make and repair type, with parts, labor, and total ranges in USD. It is specific about the resource and action, but it does not explicitly differentiate itself from the sibling tool get_repair_cost_guide, which could plausibly cover similar territory.
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 provides explicit guidance to use list_makes_models and list_repair_types to discover valid slugs, which is practical for invocation. However, it does not state when to prefer this tool over get_repair_cost_guide or other sibling tools, so the when-to-use guidance is only partially addressed.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.