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

Annotations already declare readOnlyHint=true and destructiveHint=false, and the description adds valuable behavioral context: cost per invocation (1 IU), internal routing through kirk_score_book_batch with no double-metering, and the caveat that synthetic data should not appear in customer-visible results. This goes beyond what annotations provide and sets clear expectations.

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 well-structured with clear sections (Purpose, Use when, Do not use when, Capability, Path fit, Cost) and front-loaded with the primary purpose. It is slightly longer than strictly necessary but every section provides distinct value, making it efficient rather than verbose.

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 an output schema and rich annotations, this description is exceptionally complete: it covers purpose, usage alternatives, cost, internal routing, and restrictions. It explains the wrapper relationship and the non-production nature, leaving no critical gaps. The output schema handles return values, so the description's coverage is sufficient.

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 100%, with each parameter (seed, model_id, n_samples) having clear descriptions and defaults. The description mentions 'N' in the text, aligning with n_samples, but adds no additional parameter-specific semantics beyond what the schema already provides. 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?

The description clearly states the tool's function: 'Synthesize N realistic-geometry L2 book snapshots and score them' and explicitly identifies it as a convenience wrapper on kirk_score_book_batch. This distinguishes it from siblings like kirk_score_book_batch (which scores real data) and provides a specific verb+resource+scope.

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 explicit 'Use when' and 'Do not use when' guidance, including use cases (wiring-check, first-integration walk-through) and exclusions (scoring anything real, synthetic bids/asks not for customer-visible results). It also names the alternative tool (kirk_score_book_batch) directly.

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