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Talpy Aya – AI recruiter that interviews on WhatsApp

List candidates

listar_candidatos
Read-only

Lists the candidates of a job with their interview score, profile fit and pipeline stage, highest score first. Read-only. Does NOT return phone, e-mail or résumé.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
jobIdYesJob id, from listar_vagas.
stageNoFilter by pipeline stage (NOVO = new, CONVIDADO = invited, EM_ENTREVISTA = interviewing, AVALIADO = evaluated, APROVADO = approved, REJEITADO = rejected). Defaults to all.
languageNoOptional. Language for the text in this tool's response (labels, notes, messages). Defaults to the account's language.
minScoreNoOnly candidates whose interview score is at least this value (0 to 10).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed8 schema fields changed
    • removedInput schema / properties / etapa
      Removed value: -{
      -  "description": "Filtra pela etapa. O padrão é todas.",
      -  "enum": [
      -    "NOVO",
      -    "CONVIDADO",
      -    "EM_ENTREVISTA",
      -    "AVALIADO",
      -    "APROVADO",
      -    "REJEITADO",
      -    "todas"
      -  ],
      -  "type": "string"
      -}
    • addedInput schema / properties / jobId
      Added value: +{
      +  "description": "Job id, from listar_vagas.",
      +  "type": "string"
      +}
    • addedInput schema / properties / language
      Added value: +{
      +  "description": "Optional. Language for the text in this tool's response (labels, notes, messages). Defaults to the account's language.",
      +  "enum": [
      +    "pt-BR",
      +    "pt-PT",
      +    "en",
      +    "es",
      +    "de",
      +    "fr"
      +  ],
      +  "type": "string"
      +}
    • addedInput schema / properties / minScore
      Added value: +{
      +  "description": "Only candidates whose interview score is at least this value (0 to 10).",
      +  "type": "number"
      +}
    • removedInput schema / properties / nota_minima
      Removed value: -{
      -  "description": "Só candidatos com nota da entrevista igual ou acima deste valor (0 a 10).",
      -  "type": "number"
      -}
    • addedInput schema / properties / stage
      Added value: +{
      +  "description": "Filter by pipeline stage (NOVO = new, CONVIDADO = invited, EM_ENTREVISTA = interviewing, AVALIADO = evaluated, APROVADO = approved, REJEITADO = rejected). Defaults to all.",
      +  "enum": [
      +    "NOVO",
      +    "CONVIDADO",
      +    "EM_ENTREVISTA",
      +    "AVALIADO",
      +    "APROVADO",
      +    "REJEITADO",
      +    "all"
      +  ],
      +  "type": "string"
      +}
    • removedInput schema / properties / vaga_id
      Removed value: -{
      -  "description": "id da vaga",
      -  "type": "string"
      -}
    • changedInput schema / required
      Previous value: -[
      -  "vaga_id"
      -]New value: +[
      +  "jobId"
      +]
  2. First observed

TDQS

A3.8/5.0
Behavior4/5

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

'Read-only' merely repeats readOnlyHint=true so earns no credit, but 'Does NOT return phone, e-mail or résumé' is a genuinely useful privacy/scope disclosure the annotations do not carry, as is the 'highest score first' ordering guarantee. No pagination or result-size behavior is described, keeping it off a 5.

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?

Three short, front-loaded sentences: what it lists, safety posture, and what it withholds. Zero filler and the most decision-relevant information comes first.

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 usefully names the returned fields and the exclusions, which is exactly what an agent needs to interpret results. It omits any note on pagination or result limits for a list endpoint, so it is strong but not fully complete.

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% with enums fully annotated for stage and language, so the schema already does the heavy lifting. The description adds no syntax or format detail for jobId, minScore or stage filtering beyond what is documented, so the baseline 3 applies.

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?

States a specific verb and resource ('Lists the candidates of a job') and front-loads the payload ('interview score, profile fit and pipeline stage') plus the sort order. It is implicitly distinguishable from listar_vagas and detalhar_candidato, but no sibling is named explicitly, so it falls short of the top mark.

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

Usage Guidelines3/5

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

The scope ('of a job') implies when to use it, and the schema ties jobId back to listar_vagas, but the description itself gives no when-to-use/when-not guidance and never points to detalhar_candidato for single-candidate detail. Usage is inferable, not stated.

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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