design_confirm

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It fails to explain side effects (e.g., whether confirmation is recorded, if revisions replace existing data), return format, or any state changes. The phrase 'evaluate lessons' hints at processing but lacks detail on outcomes or requirements.

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 a single sentence, which is concise in length, but it lacks clarity and structure. It front-loads multiple ambiguous actions instead of stating a primary purpose. While not verbose, the conciseness is not effective because it sacrifices comprehensibility.

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 tool has 4 parameters (2 required) and an output schema, the description is grossly inadequate. It doesn't specify required inputs, optional behavior, or what the tool returns. An agent would need to open the schema and infer semantics from parameter names alone, which is risky. The description adds minimal context beyond the schema.

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 0%, so the description must explain all parameters. It indirectly references confirmation_text and review_revision_text ('confirm' and 'revise its pending review'), but it does not clarify design_id (which is required) or lesson_candidates_json (which is a JSON string with default '[]'). The description fails to define the role of each parameter or their expected formats.

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 'Confirm the model, evaluate lessons, or revise its pending review' is vague and ambiguous. It lists three verbs but doesn't clearly define the resource ('the model' is unclear) or how these actions relate. It doesn't differentiate from sibling tools like design_lesson_decide or design_status, leaving an agent uncertain about the tool's exact function.

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 provides no guidance on when to use this tool versus alternatives. It doesn't mention any conditions, prerequisites, or exclusions. An agent cannot determine whether to call design_confirm or design_lesson_decide from the description alone.

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