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fugu_orchestrate

Orchestrates local AI model workers to complete tasks: choose a single worker in fast mode or run a multi-agent workflow with shared memory and synthesis in ultra mode.

Instructions

Executa uma orquestracao local estilo Fugu: modo fast seleciona um worker; modo ultra cria workflow com model_id, subtasks, access_list, memoria compartilhada e sintese.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cwdNoDiretorio de execucao dos workers.
modeNofast seleciona um worker; ultra executa workflow multiagente.ultra
taskYesTarefa completa que deve ser roteada ou decomposta.
effortNoProfundidade do workflow em modo ultra.high
timeoutMsNoTimeout por worker em milissegundos.
workerPoolNoModelos Antigravity permitidos para workers.
excludeModelsNoModelos a excluir do pool por privacidade, custo ou compliance.
sharedMemoryKeyNoChave de memoria compartilhada em processo para workflows relacionados.
includeRawOutputsNoInclui stdout bruto de cada worker no structuredContent.
Behavior2/5

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

With no annotations provided, the description carries the burden of explaining side effects and runtime behavior. It mentions local execution and shared memory, but omits details about disk writes, network calls, rate limits, or the final synthesis result. The description gives a high-level overview but lacks depth for a tool that likely manages multiple workers and shared state.

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 a single sentence that packs the key mode distinction. It is front-loaded and contains no filler, making it concise and easy to read.

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?

Despite the tool's complexity (9 parameters, two enums, no output schema), the description gives only a brief overview. It does not explain return value shape, error scenarios, or how the workflow synthesis is structured. The description leaves many operational details unaddressed, making it incomplete for such a feature-rich tool.

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?

The input schema has 100% parameter descriptions, so the baseline is 3. The description adds a little extra meaning by linking 'memoria compartilhada' to shared memory and 'sintese' to synthesis, but it also introduces terms like 'model_id', 'subtasks', and 'access_list' that are not present in the schema, which could confuse. Overall, the schema already does the heavy lifting.

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 executes a Fugu-style local orchestration with two modes (fast and ultra). It differentiates the modes, and the verb 'Executa' plus the resource 'orquestracao local estilo Fugu' identifies the purpose. It does not explicitly contrast with sibling tools, but the orchestration role is distinct enough.

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 description conveys that fast mode selects a worker and ultra mode creates a multi-agent workflow, giving some guidance on when to use each mode. However, it provides no explicit exclusions or alternatives, and does not discuss prerequisites or integration context relative to sibling tools like run_antigravity or compare_models.

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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