Accept suggestion

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

The description discloses side effects: for memory_correction it writes a behavior correction and refreshes the persona, and for automation/todo it attempts creation or returns executable instructions. The idempotentHint annotation is not contradicted. No hidden behaviors.

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 moderately concise, containing three sentences that pack essential details without excessive fluff. It is structured logically by type, which aids readability.

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 the presence of sibling tools, the description sufficiently differentiates suggest_accept by explaining the accept action and its consequences. It covers the main branches of behavior and mentions the output fallback, making it complete for a tool of this complexity.

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?

The schema already provides clear descriptions for id and host, and the tool description reinforces the source of id and lists example host names. While the description does not add much beyond the schema, it is consistent and clear.

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 tool's purpose: 'Accept a suggestion' and explicitly distinguishes between memory_correction and automation/todo types with specific actions for each. The verb and resource are unambiguous.

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 explains what happens for each suggestion type and notes the host requirement for fallback wording. It implies when to use this tool but does not explicitly contrast it with sibling tools like suggest_ignore or suggest_list, which could enhance guidance.

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