update_ai_act_control

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

With no annotations, the description carries the full burden. It discloses that the operation is a write operation and is audited, which is useful. It also implies the control_id should come from list_ai_act_controls. However, it does not specify whether updates are atomic or what happens if a field is omitted.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise, front-loading the purpose and then listing arguments in a clear block. Every sentence adds value with no redundancy.

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

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

Given no output schema, the description does not specify the return value upon success or failure. However, it covers the operation well for an update tool with simple parameters. It could mention expected outcome (e.g., 'Returns updated control') but is still fairly complete.

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

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

Although the schema has 0% description coverage, the tool description compensates by providing detailed explanations for all four parameters, including acceptable values for status and optionality for notes and evidence_url.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action (update) and the resource (AI Act control), and specifies which fields can be updated (status, notes, evidence URL). It is distinct from sibling tool list_ai_act_controls which lists controls.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies this tool is for updating controls, but does not explicitly state when to use it over alternatives like update_agent_model_rule or other update tools. It notes it is a write operation and recorded in audit log, which provides context but no exclusion criteria.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.