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search_conversations

Search conversations by message text, phone number, or date range to locate specific customer interactions.

Instructions

Buscar conversaciones — Busca conversaciones por texto, telefono o rango de fechas. Ejemplo: 'busca conversaciones que mencionen devolucion' [query]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryNoTexto a buscar en los mensajes de las conversaciones
phoneNoNumero de telefono del cliente (con o sin +)
date_fromNoFecha inicio (YYYY-MM-DD)
date_toNoFecha fin (YYYY-MM-DD)
limitNoMaximo de resultados (default 20, max 50)
offsetNoPosicion de inicio para paginacion
hoursNoUltimas N horas
conversation_idNoID de conversacion especifica
last_nNoUltimos N mensajes
periodNoPeriodo de tiempo
agent_idNoID del agente
messageNoTexto del mensaje a enviar o eliminar
formatNoFormato de respuesta
Behavior2/5

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

No annotations are provided, and the description does not disclose behavioral traits such as pagination behavior, rate limits, data scope, or output format. It only mentions search criteria, which is already implied by the name and schema.

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?

The description is concise (two sentences plus an example) and front-loaded with the title and core action. No extraneous content, but could be more structured with explicit sections.

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

Completeness2/5

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

Given 13 parameters and no output schema, the description lacks completeness. It does not explain parameter interactions, default behaviors, or response structure, leaving significant gaps for an AI agent.

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 coverage is 100% with all parameters described in the schema. The description adds an example but does not enrich parameter meaning beyond the schema. Baseline 3 is appropriate.

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 clearly states the tool searches conversations by text, phone, or date range, with an example. It specifies the resource (conversations) and criteria, but does not explicitly differentiate from similar sibling tools like search_customer_comments or global_search.

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

Usage Guidelines2/5

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

No guidance on when to use this tool versus alternatives. The description provides only a usage example, lacking context for when to choose search_conversations over other search tools or when not to use it.

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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