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

Goes well beyond the annotations: it discloses that the call is metered against a monthly synthesis quota, that it consumes a scarcer quota than AI calls, that it fails with PLAN_REQUIRED on Free accounts, and that generated designs can carry wrong part parameters. Annotations only give the generic non-read-only, non-idempotent profile; the description supplies the operational consequences.

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 purpose and example are front-loaded, and each subsequent sentence adds distinct operational information (quota, validation chain, plan gate) rather than restating the first. It is dense but no sentence is filler; the only mild cost is that quota, validation, and plan rules are packed into one block.

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 one-parameter generation tool with no output schema, the description covers the essentials: what it returns at a high level ('already solved'), what it costs, who can call it, and what must be done with the result before trusting it. It could say slightly more about the shape of the returned design, but the validation chain largely compensates.

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 coverage is 100% and the single 'prompt' parameter is fully described in the schema, so the baseline is 3. The description adds value by giving a concrete example brief and emphasizing what must be included (input voltage, every rail with its current, binding constraints), which helps the agent compose a usable prompt rather than a vague one.

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?

States a specific verb and resource ('Generate a complete power tree from a plain-English brief') and concretizes it with a realistic brief example, including that the result is returned 'already solved'. It also names the siblings it relates to (get_account_usage, solve_design, validate_design), so an agent can place it in the workflow without ambiguity.

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?

Gives clear usage conditions: check get_account_usage first because the synthesis quota is smaller than the AI quota, and always follow with solve_design and validate_design before presenting results. It also states the plan prerequisite. What it lacks is routing guidance against the sibling generation tool create_design, which an agent might otherwise confuse with this one.

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