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wganalytics

mcp-whatsapp-giulia-ai

by wganalytics

get_group_messages

Retrieve WhatsApp group messages within a specified date range, returning sender, timestamp, message type, and text for each entry.

Instructions

Recupera as mensagens enviadas em um grupo do WhatsApp dentro de um intervalo de datas especificado. Esta ferramenta permite ao agente acessar o histórico de conversas de um grupo, retornando as mensagens publicadas entre 'start_date' e 'end_date', com detalhes como remetente, horário, tipo da mensagem e conteúdo textual. Args: group_id (str): Identificador único do grupo do WhatsApp. start_date (str): Data e hora de início no formato 'YYYY-MM-DD HH:MM:SS'. end_date (str): Data e hora de término no formato 'YYYY-MM-DD HH:MM:SS'. Returns: str: Lista de mensagens formatadas, com os campos: - Usuário - Data e hora - Tipo da mensagem - Texto Cada mensagem é separada por um delimitador visual.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
end_dateYes
group_idYes
start_dateYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations are provided, so the description must carry the full behavioral burden. It discloses the return format (list of messages with user, datetime, type, text) and the date-range filtering, but does not mention any potential side effects, rate limits, or limitations. Since this is a read operation, the lack of explicit safety disclosure is a minor gap.

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 well-structured and front-loaded with purpose, followed by parameter details and return format. It contains no redundant sentences; every sentence contributes useful information about what the tool does, how to use it, and what it returns.

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?

The description covers the tool's input parameters, date format, and output structure (including fields and delimiter). Even though an output schema is indicated, the Returns section adds extra clarity. For a moderate-complexity read tool, this is complete and self-sufficient.

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

Parameters5/5

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

The schema only provides types and titles, with 0% description coverage, but the description's Args section gives each parameter meaningful explanations: group_id as unique group identifier, and start_date/end_date with explicit format 'YYYY-MM-DD HH:MM:SS'. This fully compensates for the schema's lack of detail.

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 'Recupera as mensagens enviadas em um grupo do WhatsApp' (retrieves sent messages in a WhatsApp group), using a specific verb and resource. It distinguishes from siblings like get_groups (which lists groups) and send_* tools (which send messages).

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?

The description explains the tool's purpose as accessing group conversation history within a date range, clearly implying its use for read operations. It does not explicitly name alternatives or exclusions, but the context is unambiguous and adequate.

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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