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Glama

get_messages

Retrieve messages from any WhatsApp chat or group within a time range. Audio is returned as transcribed text and media is saved to local files, giving AI agents a complete readable history.

Instructions

Lê as mensagens de uma conversa em uma janela de tempo, já com os áudios transcritos.

chat: nome do contato/grupo, número (5562...) ou identificador completo. since/until: 'hoje', 'ontem', '3d', '2h', '10/09', '10/09 14:30', '2026-09-10'. Tambem aceita intervalo em since: '10/09..12/09'. Vazio = mais recentes. transcrever_audios: áudios voltam como texto (ElevenLabs Scribe v2). baixar_midias: imagens/vídeos/documentos são gravados em disco e o caminho vem no transcript, pronto para o Read. tipo_mensagem: filtra por tipo, ex 'AudioMessage', 'ImageMessage', 'DocumentMessage'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
chatYes
sinceNo
untilNo
limiteNo
instanceNo
apenas_nossasNo
baixar_midiasNo
tipo_mensagemNo
transcrever_audiosNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.7/5.0
Behavior4/5

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

No annotations are present, so the description carries the disclosure burden. It usefully discloses that audio comes back transcribed via ElevenLabs Scribe v2 and that media files are written to disk with the path returned in the transcript for the Read tool. It does not mention pagination, ordering, or instance/auth behavior, but the main side effects are surfaced.

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?

The description is compact, front-loaded with the purpose, and organized as a short parameter reference. Every line adds useful information, and there is no filler or redundant restatement of the tool name.

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

Completeness3/5

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

The definition covers the operation, key parameter formats, and side effects, but with 9 parameters and no annotations it is not fully complete: limite, instance, and apenas_nossas remain undocumented. Since an output schema exists, return values do not need to be described, so the gap is manageable but still 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?

With schema description coverage at 0%, the description is the only source of parameter meaning. It documents chat, since/until with exact formats, transcrever_audios, baixar_midias, and tipo_mensagem with examples. However, limite, instance, and apenas_nossas are left unexplained, which is a significant gap.

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 opens with a specific verb and resource: reading messages from a conversation within a time window, with audio transcriptions. It clearly conveys the retrieval purpose and likely scope, but it does not explicitly distinguish this from sibling tools such as search_messages.

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 first sentence implies the use case: retrieving conversation messages by time range, with optional transcription and media download. However, it does not provide explicit when-to-use versus alternatives guidance, nor any exclusions such as 'for searching, use search_messages instead.'

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.