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

With no annotations provided, the description carries the burden of behavioral disclosure. It reveals a critical anti-fabrication behavior: rates/distributions are verified .aidos-file values and must be quoted verbatim, not estimated or replaced with training-data recall. This adds important context beyond a simple read operation.

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 concise (three sentences) and front-loaded: purpose, usage, and a critical behavioral rule. Every sentence adds value with no 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 a single-parameter, no-output-schema tool, the description provides sufficient context: what facts are returned, when to use it, and how to handle the data (verbatim quoting). It is complete for its complexity and differentiates well from siblings.

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?

The input schema already provides 100% coverage of the 'chapter' parameter, including enum values, format, default, and the ct vs leds distinction. The tool description does not add any additional parameter-specific meaning beyond referring to the 'selected chapter,' so it stays at the baseline.

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 specifies the tool's function: returning structural facts (topology, rate limits, interrupt distributions, expected efficiency) for the selected chapter. It distinguishes from siblings like get_chapter_narrative by focusing on structural/configuration data rather than narrative.

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 explicitly states the usage trigger: 'Use when the user asks about the line's configuration.' It provides clear context but does not mention exclusions or alternatives, stopping short of a full when/when-not contrast.

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