get_market_valuation

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden for behavioral disclosure. It discloses the exact statistics returned, the tolerance for mileage ('±30% band'), the credit cost per call, and that failed calls are auto-refunded. However, it does not mention potential error scenarios beyond failed calls or any rate limits.

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 four well-structured sentences that stay on topic: purpose, name flexibility, filters, and cost/refund policy. Every sentence provides unique, useful information without redundancy, and the key purpose is front-loaded.

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 covers the essential invocation details: required fields, accepted input variants, valid filter values, credit cost, and refund behavior. The presence of an output schema means return values need not be explained. It does not mention alternative tools or edge cases like missing data, but it is sufficiently complete for correct use.

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 no descriptions or enums, but the description compensates by clarifying how to format model names ('VW Golf', 'BMW 3 Series' or '320d'), providing allowed fuel types ('petrol/diesel/hybrid/electric/lpg or Polish values'), gearbox values, and mileage meaning with a percent tolerance. This adds essential semantics beyond the raw parameter names.

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's purpose: 'Get average, median, P25, and P75 prices for a specific make/model/year in Poland.' This is a specific verb+resource combination, and the distinct statistics distinguish it from sibling tools like get_price_history or get_market_liquidity, which focus on different aspects.

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 usage hints like 'Natural names work' and optional filters, but it does not explicitly contrast with sibling tools or state when to prefer this valuation tool over alternatives like get_price_history or get_regional_pricing. The usage context is implied through the described output and filtering options.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.