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

Annotations declare readOnlyHint=false and destructiveHint=false but say nothing about cost, review mechanics, or output structure. The description fills that gap with high-value behavior: metered quota consumption, worst-case input derivation, the `rejectedSuggestions`/`ratingCheck` filtering behavior, and the specific limitation that solve results are only checked at one operating point. This is exactly the kind of detail annotations cannot carry.

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?

Front-loaded with the core action ('Ask Electronics Architect's own reviewer...'), then progressively details output, cost, and worst-case mechanics. Dense but nearly every sentence earns its place; the worst-case paragraph runs slightly long but each clause adds behavioral information unavailable elsewhere.

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 complex, metered, open-world-ish analysis tool with no output schema, the description covers purpose, cost, input semantics, internal review mechanics, output structure (both the narrative and the rejectedSuggestions list), and the key edge case (solve-result limitation). An agent has everything it needs to call it correctly and interpret the result.

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 both params are described inline, so the schema already carries param syntax. The description adds semantic context for `design` (a design document preferred over a solve result, and the consequences of each), but provides no further detail on `ambientTemperature` beyond what the schema states. Baseline 3 is appropriate.

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+resource ('AI design review of a design or solve result') and enumerates exactly what it returns: plain-English interpretation of numbers followed by concrete improvements with designators and estimated gains. It distinguishes itself from siblings like solve_design and validate_design by framing the output as advisory suggestions rather than a solve or a pass/fail check.

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?

Clearly states what to pass ('the design document: the reviewer solves it itself') and notes the solve-result alternative and its limitation, along with the cost ('one call on the account's monthly AI quota'). It lacks explicit when-not-to-use guidance or direct naming of the alternative siblings (e.g., run_derating, validate_design) for narrower needs.

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