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update_agent

Update an existing agent's configuration by modifying its name, model, provider, instructions, capabilities, memory scope, policy profile, or config.

Instructions

Update an existing agent's configuration.

Args: agent_id: Agent to update. name: New agent name. model: New LLM model name. provider: New LLM provider. instructions: New system prompt / instructions. capabilities: New list of enabled capability pack names. memory_scope: New memory scope. policy_profile: JSON string of policy profile configuration. config: JSON string of additional configuration.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNo
modelNo
configNo
agent_idYes
providerNo
capabilitiesNo
instructionsNo
memory_scopeNo
policy_profileNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

Annotations indicate readOnlyHint=false and destructiveHint=false, which the description supports by stating 'update' (mutation but not destructive). The description lists configurable fields but does not disclose additional behaviors like permission requirements, side effects, or whether partial updates are allowed. With annotations already covering the basic safety profile, the description adds modest value.

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, with a front-loaded purpose sentence followed by a compact Args list. Each parameter explanation is minimal but sufficient. It could be slightly more structured (e.g., grouping required vs optional) but overall is efficient and scannable.

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 tool's complexity (9 parameters, 1 required) and the presence of an output schema, the description adequately explains inputs. However, it does not mention prerequisites (e.g., agent must exist), error scenarios, or the relationship to other agent lifecycle tools. For a typical CRUD update, this is sufficient but not exhaustive.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description compensates by providing one-line explanations for each parameter (e.g., 'agent_id: Agent to update', 'name: New agent name'). These clarify the purpose of each parameter beyond the bare type information in the schema. However, it lacks details on allowed values, formatting, or constraints (e.g., whether fields are optional or nullable is already in 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?

The description clearly states 'Update an existing agent's configuration', which is a specific verb (update) and resource (agent configuration). This distinguishes from sibling tools like create_agent, delete_agent, get_agent, and list_agents, making the tool's purpose unambiguous.

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 implies usage when modifying an existing agent but does not explicitly state when to use this tool over create_agent (for new agents) or delete_agent (for removal). No alternatives or when-not conditions are mentioned, leaving the agent to infer the correct context.

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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