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

With no annotations provided, the description carries the full behavioral disclosure burden. It reveals meaningful traits: parallel execution of four models, a ~15-30s latency budget, and a synthesized output with an alignment signal. The only gap is that it never states whether the call has side effects (e.g., logging, gating) or is purely a read/compute operation, which matters given the gate_* and record_* sibling tools suggest a pipeline context.

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?

Four sentences, each earning its place: trigger condition, example use cases, execution mechanism plus latency, and routing to alternatives. The key decision signal ('multi-model second opinion fast') is front-loaded, and the sibling routing is compactly delivered at the end. Zero filler.

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 the tool-selection decision (when to call this vs siblings), the description is essentially complete. But the tool has 10 undocumented parameters and no output schema, and the description only vaguely gestures at what inputs are expected and what the return looks like beyond 'one answer with an alignment signal.' Given the parameter complexity, this leaves material gaps in what an agent needs to invoke it correctly.

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% across 10 string parameters, so the description must compensate and largely does not. It hints that the question/content concerns coding decisions, which loosely maps to the `question` param, but diff, context, gate_*, continuation_token, and target_hunk_hashes are never explained or even acknowledged. An agent receives almost no help understanding what to populate for this tool.

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 leads with a specific verb+resource: 'multi-model second opinion fast' for coding questions, and explicitly names the mechanism ('Four frontier models answer in parallel... synthesized into one answer with an alignment signal'). It distinguishes itself from siblings by naming deliberate_coding and audit_coding as the alternatives for different stakes, so an agent can tell them apart without opening any schema.

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?

Provides explicit when-to-use guidance ('when you want a multi-model second opinion fast', 'where being right matters but you don't have time for full deliberation'), concrete example scenarios (library/API choices, idiomatic patterns), and explicit exclusions with named alternatives ('For high-stakes decisions reach for deliberate_coding; for stress-testing a draft answer reach for audit_coding'). Nothing is left to inference.

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