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Talpy Aya – AI recruiter that interviews on WhatsApp

Create job

criar_vaga

Creates a new job opening in the company's Talpy account and generates the interview profile Aya will use (competencies and weights). By default the job is created as a DRAFT (not public); set publish=true to open it right away. Counts against the plan's open-job limit. Requires an API key with the mcp:write scope. Returns in a few seconds with the job id, its status, the dashboard link and profileStatus: "generating" means the full competency profile is still being built in the background (usually under a minute) — call detalhar_vaga later to see it.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
titleYesJob title, e.g. "Sales Representative".
publishNoOptional. true = publish the job now (status ATIVA, open to applications). Default false = draft (RASCUNHO) for a human to review.
languageNoOptional. Language of the job: Aya opens the candidate interview in it. Also used for the text of this response. Defaults to the account's language.
seniorityNoOptional seniority level, e.g. "Junior", "Mid-level", "Senior".
descriptionYesJob description (at least 20 characters): responsibilities and requirements. Aya's evaluation criteria are derived from it.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.4/5.0
Behavior5/5

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

Goes well beyond the annotations: discloses the plan's open-job limit consumption, the required mcp:write API scope, expected latency ('a few seconds'), and the background profileStatus generation lifecycle. Annotations only supply the generic non-idempotent/non-destructive flags, so this added context is substantial.

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?

Front-loaded with the core action and default state, then layers on limits, auth scope, and return behavior. Dense but every clause carries operational value; a slightly longer sentence about profileStatus could be trimmed.

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?

With no output schema, the description compensates by describing the return payload (job id, status, dashboard link, profileStatus) and the async generation follow-up. An agent has everything needed to invoke and interpret the result.

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%, so title, publish, language, seniority and description are already documented with defaults and examples. The description reinforces the publish default and the language effect on the response text, but adds little syntax or meaning beyond the schema.

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?

States a specific verb and resource ('Creates a new job opening') and adds a non-obvious side effect (generates the interview profile Aya will use). It is clearly distinguishable from siblings like detalhar_vaga and listar_vagas, which are read-only lookups.

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?

Explains the default behavior (draft, not public) and the publish=true alternative for immediate opening, plus routing guidance to call detalhar_vaga after profileStatus is 'generating'. It lacks explicit when-not-to-use conditions, but the operational context is clear.

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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