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Talpy Aya – AI recruiter that interviews on WhatsApp

Send candidate to Aya's interview

entrevistar_candidato

Adds a candidate to a job and immediately invites them to an interview with Aya, the AI recruiter. SIDE EFFECTS: with a phone number it sends a real WhatsApp invite to the candidate; without a phone but with an e-mail it sends a real e-mail with the link to the web interview. Uses one interview from the company's balance when the interview happens. Requires an API key with the mcp:write scope. Only invite people who applied or agreed to be contacted. Returns candidateId, interviewId and the invite channel.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesCandidate's full name.
emailNoCandidate's e-mail. Used for the invite (web interview link) when there is no phone.
jobIdYesJob id, from listar_vagas or criar_vaga.
phoneNoCandidate's WhatsApp number in international format with country code, e.g. +5511912345678. If given, the invite goes by WhatsApp.
languageNoOptional. Language for the text in this tool's response (labels, notes, messages). Defaults to the account's language.
resumeTextNoOptional résumé as plain text. Used to pre-score the candidate against the job profile.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.7/5.0
Behavior5/5

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

Despite annotations covering the safety profile, the description adds rich behavioral context beyond them: real WhatsApp/e-mail invites are sent, one interview is deducted from the company balance, and an API key with mcp:write scope is required. This is exactly the kind of side-effect disclosure a mutation tool needs.

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?

Front-loaded with the core action, followed by a clearly labeled SIDE EFFECTS segment and return values. Every sentence earns its place with no filler.

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?

Though no output schema exists, the description lists the returned fields (candidateId, interviewId, invite channel), and it covers side effects, auth scope, and channel behavior. An agent has everything needed to invoke it correctly.

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?

Schema coverage is 100%, so a baseline of 3 applies, but the description adds real meaning by tying phone presence to WhatsApp delivery and e-mail to a web-interview link, which is the routing logic the agent must understand. It does not add syntax detail 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?

The description states a specific verb and resource: adds a candidate to a job and invites them to an interview with 'Aya'. It is clearly distinguishable from siblings like criar_vaga or listar_candidatos, which handle job creation and listing rather than candidate invitation.

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 gives clear usage context ('Only invite people who applied or agreed to be contacted') and explains the channel-selection logic. It does not, however, explicitly name alternatives for cases where the agent should do something else (e.g., creating a job first via criar_vaga), leaving that 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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