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list_models

Models for a given brand that currently have active deals, with counts.

Input Schema

NameRequiredDescriptionDefault
brandYesBrand name (required)
fuelsNoFuel types, e.g. ["diesel","benzín","elektro","hybrid"]
yearMaxNo
yearMinNo
priceMaxNoMaximum price in CZK
priceMinNoMinimum price in CZK
bodyTypesNo
countriesNoMarkets to include (default CZ)
maxMileageNoMaximum mileage in km
minDiscountNoMinimum % below market median (e.g. 20)
priceDroppedNoOnly listings whose price recently dropped

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

C2.7/5.0
Behavior2/5

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

No annotations exist, so the description carries the full burden, and it discloses almost nothing: not whether the counts are deal counts, whether results are paginated, how filtering across the 11 optional parameters combines, or whether only models with active deals exclude models without them. 'With counts' is the only behavioral hint.

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

Conciseness4/5

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

A single short sentence with no filler; the scoping to brand and the 'with counts' detail are front-loaded. It is efficient, if under-informative rather than bloated.

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

Completeness2/5

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

For an 11-parameter tool with no annotations and no output schema, the description is far too thin: it never explains the filter semantics, the meaning of the returned counts, or result ordering/limits. The only richness comes from the input schema itself.

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

Parameters2/5

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

Schema description coverage is 73%, leaving yearMin, yearMax, and bodyTypes undocumented in the schema, and the description adds nothing about any of the 11 parameters beyond confirming that brand is the pivot. It does not explain how filters like minDiscount, priceDropped, or countries interact with the 'active deals' constraint.

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

Purpose4/5

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

The fragment clearly identifies the resource (models) and the scope (models for a given brand that currently have active deals, with counts), which is distinguishable from the sibling list_brands. It lacks an explicit verb, but 'Models for a given brand...' plus 'with counts' tells an agent what set is returned.

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

Usage Guidelines2/5

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

The phrase 'that currently have active deals' implies when the tool is useful, but there is no explicit when-to-use guidance, no exclusions, and no reference to alternatives such as search_deals or list_hot_deals. An agent must infer the routing decision entirely.

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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