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GTA7-Lab

gta7-lab-city

by GTA7-Lab

Orquestrar pedido

orchestrate

Accepts natural language requests, selects tagged entities, calls their tools in parallel, applies constraints (people, budget), and returns combined results per entity with cross-entity combinations.

Instructions

Recebe um pedido em linguagem natural, escolhe as entidades pelas tags, chama as MCP tools delas em paralelo, aplica as restrições (pessoas, orçamento por pessoa) e devolve os resultados por entidade mais combinações entre entidades.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoitens por entidade; padrão 5
requestYesex.: 'Quero jantar e depois fazer alguma atividade'

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

A4/5.0
Behavior4/5

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

No annotations are present, and the description takes on the burden by explicitly disclosing parallel execution, tag-based entity selection, constraint handling, and the return shape (per entity plus combinations). It does not discuss failure handling or side effects of the invoked tools, but the core runtime behavior is transparent.

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?

One dense sentence communicates all core behavior with no filler and is front-loaded with the main action. It could be split for readability, but it is appropriately sized.

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 description includes return behavior and mentions constraints, and the schema covers inputs, which is enough for basic invocation. It leaves open details such as how constraints are expressed in the request, error behavior, and the exact result structure, and there is no output schema to fill those gaps.

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 request and limit are already documented with examples and defaults. The description adds little at the parameter level beyond the general natural-language context, so the schema baseline of 3 applies.

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 opens with a specific verb and resource ('Recebe um pedido em linguagem natural') and spells out a distinctive orchestration flow: tag-based entity selection, parallel MCP tool calls, constraint application, and result aggregation. This clearly separates it from direct entity tools like get_entity/update_entity and from plan_request.

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 defines its input as a natural-language request and states that it orchestrates multiple entities and constraints, so the intended use is evident for multi-entity requests. It does not explicitly contrast itself with siblings such as call_entity_tool or plan_request, but the context is clear enough.

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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