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Cambio Uruguay

by io.github.eduair94
OverviewDiscussionsAdmin

Precios de un modelo por año y versión

car_model_prices
Read-onlyIdempotent

Cuánto se pide por un modelo: mediana y rango por año y por versión (motor y caja), la guía de precios de Mercado Libre, las oportunidades del modelo y los más baratos (de un año si se indica). Para '¿cuánto vale un Onix 2019?' o '¿a cuánto vendo mi auto?'.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "brand": "<brand>",
      "model": "<model>"
    },
    "name": "car_model_prices"
  }
}

Input Schema

NameRequiredDescriptionDefault
yearNoAño, para ver los más baratos de ese año.
brandYesMarca: "Chevrolet".
modelYesModelo: "Onix".

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changed
    • addedInput schema / properties / brand / description
      Added value: +"Marca: \"Chevrolet\"."
    • addedInput schema / properties / model / description
      Added value: +"Modelo: \"Onix\"."
    • addedInput schema / properties / year / description
      Added value: +"Año, para ver los más baratos de ese año."
  2. Added

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already declare read-only, open-world, idempotent, non-destructive behavior. Beyond that, the description usefully previews output content (median/range, Mercado Libre guide, opportunities, cheapest by year), though it does not discuss data freshness or coverage limits.

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?

The purpose is front-loaded in the opening phrase, and the example queries earn their place by anchoring usage. The single long sentence is dense but has no filler; it could be slightly easier to scan if split.

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?

With no output schema, the description carries the burden of explaining return values, and it does so by naming median/range, Mercado Libre guide, opportunities, and cheapest listings. It omits caveats about data provenance or freshness, but for a read-only price lookup the core context is present.

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 coverage is 100% and all three parameters are documented in the schema, so the baseline is 3. The description reinforces the year filter ('de un año si se indica') and brand/model use via the Onix example, but adds little parameter syntax or constraints beyond the schema.

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 description states a specific resource and result set: median and range prices for a model by year and version, Mercado Libre price guide, opportunities, and cheapest listings. It is clear what data the tool returns, but it does not explicitly distinguish itself from siblings such as find_car_opportunities or car_market_report.

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?

It names concrete user questions ('¿cuánto vale un Onix 2019?' or '¿a cuánto vendo mi auto?') that map directly to this tool, so an agent knows the context. It gives no explicit when-not or alternative-tool routing, leaving sibling selection to inference.

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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