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clarify_autoparts_intent

Resolve ambiguous Spanish autoparts terms by retrieving their geometric archetype, typical dimensions, and a disambiguation question to confirm user intent before modeling.

Instructions

Devuelve interpretaciones autoparts para un término en español/spanglish.

Phase 2 / Layer 2: cuando un usuario use un término informal de autopartes ('rin', 'buje', 'soporte', 'brida', 'cubo', 'flecha', 'polea', 'tapa', 'caja', 'gancho', 'balero', etc.) y necesites confirmar qué arquetipo geométrico quiere ANTES de proponer un plan, llama esta herramienta para obtener el mapeo curado.

Devuelve:

  • primary_archetype: la interpretación más común

  • alternative_archetypes: otras lecturas razonables

  • typical_dimensions: rangos esperados (mm/grados/conteos)

  • disambiguation_question: pregunta exacta para el usuario

[en: Look up an informal Mexican-Spanish autoparts term and get its curated geometric interpretation — primary archetype + alternatives + typical dim ranges + a ready-to-ask disambiguation question — use it to ground your interpretation when the user's request hinges on an ambiguous term. v1 vocabulary is Mexican-Spanish-specific and fixed in code; v1.1 may make it customer-extensible.]

Args: term: The Spanish / Spanglish term to look up. Case- and accent-insensitive ('Rin', 'rin', 'RIN' all match).

Returns: On match: { "term": str, # canonical spelling "primary_archetype": str, # internal handle "primary_description": str, # Spanish description "alternative_archetypes": [str, ...], "alternative_descriptions": [str, ...], "typical_dimensions": {key: [min, max], ...}, "disambiguation_question": str, "notes": str, "found": True, } On miss: { "term": str, "found": False, "fallback": str, # what to do instead "available_terms": [str, ...], # what IS in the glossary }

Caveat: this v1 vocabulary is Mexican-Spanish autoparts only. Argentine, Brazilian, or Peninsular Spanish usage may differ. Customers wanting their own vocabulary need v1.1 customer-config support.

Caveat: NOT all terms have a clean primary archetype. Generic terms like 'soporte', 'caja', 'balero', 'tornillo', 'rosca' map to 'ambiguous' or 'needs_custom_modeling' — use the disambiguation_question to narrow down.

Example — disambiguating 'rin' before building geometry: intent = clarify_autoparts_intent("rin") # → primary_archetype="wheel_rim", # alternatives=["wheel_hub_disc", "brake_rotor"], # typical_dimensions={od_mm: [330, 560], pcd_mm: [98, 120], ...} # Use the disambiguation_question to confirm the reading with # the user before any geometry runs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
termYes
Behavior5/5

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

With no annotations, the description fully discloses scope (Mexican-Spanish only), caveats (regional differences, ambiguous terms), version constraints (v1 fixed vocabulary, v1.1 extensible), and the exact return structure including a fallback for missing terms.

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 description is somewhat long but well-structured with sections for purpose, caveats, and example. The first sentence establishes purpose immediately. Every sentence adds value; however, minor trimming could enhance conciseness.

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

Completeness5/5

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

Given the tool's simplicity (single parameter, no output schema, no annotations), the description adequately covers all necessary aspects: purpose, input, output structure (with fields and example values), and usage context. It is self-contained and complete.

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

Parameters4/5

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

The single parameter 'term' lacks schema description (0% coverage), but the description explains it is case- and accent-insensitive and expects a Spanish/Spanglish autoparts term. An example is provided. Slightly more detail on expected format would push to 5.

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 clearly states the tool resolves informal Mexican-Spanish autoparts terms into curated geometric archetypes, alternatives, and disambiguation questions. This distinctively sets it apart from sibling tools that perform CAD modeling operations.

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

Usage Guidelines5/5

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

The description explicitly tells the agent to call this tool 'when a user uses an informal autoparts term and before proposing a plan', referencing Phase 2/Layer 2. It gives an example ('rin') and details the disambiguation flow.

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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