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update_agent

Idempotent

Modify an existing AI agent by submitting only the fields you want to change. Update name, bio, tags, metadata, LLM config, visibility, and more.

Instructions

Atualiza campos de um agente existente. Envia apenas os campos que deseja alterar.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bioNoNova descrição/biografia
nameNoNovo nome interno
tagsNoNovas tags
typeNoNovo tipo
agent_idYesUUID do agente a atualizar
metadataNoNovos metadados em JSON livre
llm_configNoNova configuração do LLM. model: "provider.modelo" ou "auto". temperature: 0-2. fallback_chain: cadeia de fallback.
visibilityNoNova visibilidade
chat_enabledNoHabilita/desabilita o agente no chat nativo (chat.zihin.ai). Gate por agente do canal chat — default false. Vive em tenant_agents, não no agente.
commercial_nameNoNovo nome comercial
Install Server

TDQS

A4/5.0
Behavior3/5

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

Annotations already convey idempotence, non-read-only, and non-destructive behavior. The description adds the key behavioral detail that only provided fields are altered, which is useful beyond the annotations; however, it does not mention validation, rejection behavior, or what happens for omitted or empty fields.

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?

Two short, front-loaded sentences with no filler; the first identifies the action and resource, the second captures the critical usage convention.

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

Completeness4/5

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

Given the rich schema and moderate annotations, a concise pointer to partial-update semantics is sufficient for invoking the tool correctly. It does not describe return values, but there is no output schema, and this is not essential to making a correct call.

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 the schema already documents every parameter, including nested llm_config fields, enums, and defaults. The description only reinforces the partial-update convention and adds no field-level meaning beyond the schema.

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?

Clearly states the operation ('updates fields') on a specific resource ('an existing agent'), and the second sentence explicitly constrains it to a partial update. This distinguishes it from create_agent and delete_agent without needing the tool name alone.

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 partial-update instruction ('send only the fields you wish to change') gives clear operational guidance and implies the tool is for modifying existing agents rather than creating or replacing them. It does not explicitly name alternatives or exclusions, so it stops short of a 5.

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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