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

Annotations already establish read-only, idempotent, and non-destructive behavior. The description adds useful context beyond those annotations: the response is built dynamically from the loaded corpus, the call is cheap with no quota, and the tool explicitly reports what is not held in the corpus.

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 with the core purpose. Each clause adds useful information: what it reports, how it is generated, cost characteristics, and next steps. There is no wasted text.

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 zero-argument orientation tool, the description is complete. It enumerates the output contents and provides immediate next-step routing. The lack of an output schema is mitigated by the description explicitly listing what the tool will tell the agent.

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 tool has zero parameters and the schema is empty, so the baseline of 4 applies. The description does not need to explain parameter semantics because there are none.

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 identifies what the tool provides: orientation for the current corpus, including loaded regulations, unit counts, data_as_of, missing items, and first-call hints. It distinguishes itself from sibling tools by positioning itself as the entry point and naming follow-up tools like list_regulations, search_regulation, and list_workflows.

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 gives clear context for when to use this tool: start here before calling other tools. It even specifies likely next steps and notes the call is cheap and quota-free. It does not explicitly state when not to use it, but the overall guidance is strong.

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