apply_suggestion

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

No annotations provided, so description must cover behavioral traits. It describes the primary effect but misses details like side effects (e.g., overwriting previous prompt), potential errors, or required permissions. It is adequate but not fully transparent.

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?

Three sentences: first defines action, second specifies loop mode, third handles gated mode. No redundancy, front-loaded with essential info.

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?

For a tool with 2 required params and no output schema, the description covers purpose and usage mode. However, it lacks behavioral details (destructiveness, error conditions) which would be expected given no annotations. Adequate but not comprehensive.

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?

Schema description coverage is 50%, covering only 'suggestionId' with 'ID from post_prompt_suggestion response'. The description adds no additional meaning for 'workspaceId' or any parameter usage guidance.

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 states a specific action: 'Apply a pending suggestion: sets it as the active system prompt and increments the iteration counter.' This clearly distinguishes it from siblings like 'post_prompt_suggestion' (creates suggestion) and 'set_system_prompt' (direct set).

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?

Explicitly states when to call: 'Only call in fully automated loop mode (loop_optimization, loop_regression).' And when not: 'In gated mode, wait for the user to approve via the UI.' This provides clear context and exclusions.

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