openlex__zhlaw_list_laws

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

Annotations already provide readOnlyHint, idempotentHint, destructiveHint. The description adds context about sr_prefix examples, active_only filtering, pagination via offset, and return fields (no full text), which is valuable beyond annotations.

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 structured with use_case, important_notes, and example tags. It is concise, front-loaded with the main purpose, and every sentence adds value.

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?

Given complexity (4 sub-params), output schema exists, and annotations are present, the description covers when to use, parameter details, return fields, and sibling differentiation. It is complete for an AI 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 description adds concrete examples for sr_prefix (412=Bildung, etc.) and explains active_only and pagination. The schema itself has decent descriptions, but the description enhances understanding with real-world usage.

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 lists Zurich laws with optional filter, using specific verb 'auflisten' and resource 'Zürcher Gesetze'. It distinguishes itself from siblings by referencing use cases for alternative tools.

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 use_case tag explicitly states when to use (structured overview) and when not (targeted text search or pure education law), naming alternative tools search_laws and find_education_laws.

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