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Zihin MCP Server

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JavaScript
Remote

update_agent

Idempotent

Edit an existing AI agent in Zihin by sending only the fields you want to change.

Instructions

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

Input 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.
memory_enabledNoLiga/desliga a memória persistente do agente (tools save/recall/forget_memory + auto-recall). Default false. Contrato: docs/technical/MEMORY-CONTRACT.md.
commercial_nameNoNovo nome comercial
quick_replies_enabledNoPermite a tool core suggest_replies (até 4 chips de resposta rápida ao fim do turno). Só vale onde o canal renderiza: chat nativo ou integração /stream que declara capabilities.blocks=["quick_replies"]. Default true. Desligue para economizar a chamada extra ao modelo.
engagement_control_enabledNoCarrega a tool core end_engagement: o agente pode encerrar o próprio engajamento com um consumidor (gate responde mensagem fixa sem LLM até expirar). Default false. Independente de memory_enabled.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changedv2.2.2
    • addedInput schema / properties / engagement_control_enabled
      Added value: +{
+  "description": "Carrega a tool core end_engagement: o agente pode encerrar o próprio engajamento com um consumidor (gate responde mensagem fixa sem LLM até expirar). Default false. Independente de memory_enabled.",
+  "type": "boolean"
+}
    • addedInput schema / properties / memory_enabled
      Added value: +{
+  "description": "Liga/desliga a memória persistente do agente (tools save/recall/forget_memory + auto-recall). Default false. Contrato: docs/technical/MEMORY-CONTRACT.md.",
+  "type": "boolean"
+}
    • addedInput schema / properties / quick_replies_enabled
      Added value: +{
+  "description": "Permite a tool core suggest_replies (até 4 chips de resposta rápida ao fim do turno). Só vale onde o canal renderiza: chat nativo ou integração /stream que declara capabilities.blocks=[\"quick_replies\"]. Default true. Desligue para economizar a chamada extra ao modelo.",
+  "type": "boolean"
+}
  2. First observedv0.1.0

TDQS

A3.6/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=false, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds the PATCH-style semantics that only supplied fields change, which is genuinely useful, but says nothing about what happens to omitted fields or auth requirements.

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 sentences with zero waste, and the core behavior (partial update) is front-loaded. Every sentence earns its place.

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?

For a 13-parameter mutation with a nested llm_config and no output schema, the rich schema descriptions carry most of the load. The description is complete enough to invoke correctly, though it could note that omitted fields are preserved and whether the update is immediate.

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 all 13 parameters including the nested llm_config are documented in the schema itself. The description adds no field-level meaning beyond that, which is the baseline 3 when the schema 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?

States a specific verb and resource ('Atualiza campos de um agente existente'), making it clearly distinguishable from create_agent, delete_agent, and get_agent. It stops short of explicitly naming which sibling to prefer for reads or creation, so it lands at 4 rather than 5.

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?

'Envia apenas os campos que deseja alterar' gives a useful partial-update contract, but there is no explicit when-to-use guidance, no mention of prerequisites (e.g. needing the agent to exist or relevant permissions), and no routing to alternatives.

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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