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, and it does so excellently. It states that output is sim-derived with no interpretation drift, clarifies that per-chapter values are real reference values, and explains that the delta is computed rather than estimated. The explicit 'Quote VERBATIM' instruction also guides output fidelity.

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 compact and front-loaded: purpose, output nature, example usage, and anti-fabrication guidance all appear in a short, high-signal paragraph. Every sentence adds value, and the 'Quote VERBATIM' instruction reinforces the most important behavior without being padded.

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?

Despite having no output schema and no annotations, the description is complete for a low-complexity tool with two enum parameters. It names the comparison metrics, clarifies that results are simulation-derived and non-interpreted, and provides example questions that map directly to likely user intents. An agent has enough information to select and invoke the tool 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 100%, so the input schema already documents both chapter_a and chapter_b, including defaults and an especially detailed explanation for chapter_b. The description itself does not add parameter-level meaning beyond the schema, which is acceptable given the high schema coverage. The default baseline of 3 is appropriate here.

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 purpose: side-by-side comparison of two chapters with specific metrics like tracks, topology, OEE, throughput, and headline bottleneck. It also gives concrete example questions, which helps an agent understand what the tool is for. However, it does not explicitly differentiate from sibling tools such as run_gain_loss or run_buffer_tradeoff, so it stops short of full sibling differentiation.

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 cases: comparison questions, Constraint-Level vs LEDS-Level differences, and buffer-addition changes. It does not mention when not to use this tool or name alternatives from the sibling list, so exclusions and alternative routing are absent. Still, the context is clear enough for an agent to identify the appropriate scenario.

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