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Glama

Car Repair Cost Estimator

Estimate Vehicle Value

estimate_vehicle_value
Read-only

Estimate the current US market value (USD) of a vehicle from year, make, model, and optional trim. Deterministic model: base price by make, trim multiplier, age-based depreciation. Useful to judge whether a repair is worth the cost.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
makeYesMake name, e.g. "Toyota", "BMW", "Ford"
trimNoOptional trim, e.g. "Limited", "XLT", "Sport"
yearYesModel year, e.g. 2019
modelYesModel name, e.g. "Camry", "F-150"

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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 meaningful behavioral detail beyond that: the valuation is deterministic and uses base price by make, trim multiplier, and age-based depreciation. This helps the agent set expectations about consistency and methodology.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences with no filler. The core purpose is front-loaded, the input list is compact, and the methodology and use case are conveyed efficiently in the remaining sentences.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple deterministic valuation tool, the description covers inputs, output currency/value, methodology, and intended use case. There is no output schema, so the description does adequately indicate the result is a USD market value, although it does not describe error behavior for unsupported makes/models.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents each parameter with examples. The description adds a little context by noting trim is optional and contributing via a 'trim multiplier', but the schema carries most of the semantic weight, so the baseline of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource: 'Estimate the current US market value (USD) of a vehicle'. It also names the exact inputs (year, make, model, optional trim), which distinguishes it clearly from the repair-cost and vehicle-lookup sibling tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides a clear use context: 'Useful to judge whether a repair is worth the cost.' This implies when an agent should call it relative to repair-cost tools, though it does not explicitly name alternatives or state when not to use it.

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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