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

Annotations already indicate a safe, read-only, idempotent operation. The description adds context about the underlying data source (data.gov.au CKAN Action API) and the absence of authentication, which is beyond the annotations. It also discloses the specific output fields, providing useful behavioral information without contradicting the 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 two sentences with no filler. It front-loads the core purpose, then immediately gives the downstream usage, and closes with the data source and authentication status. Every sentence earns its place.

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 simple lookup tool with an output schema present and annotations covering safety, the description covers all essential information: what it returns, how to use it, and access requirements. Nothing an agent needs to call it correctly is missing.

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 provides complete descriptions for both parameters (limit and query), including defaults and examples. The tool description adds no additional parameter semantics beyond what the schema already covers, so the baseline of 3 applies.

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 states a specific verb (look up) and a well-defined resource (Australian federal/state/territory agencies/organizations on the open data portal), and explicitly names the return fields (id, slug, title). It also connects to the sibling tool au-data-gov.dataset_search by explaining that the slug is used as its `organization` filter, clearly distinguishing this from dataset search and other reference 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?

It explicitly states when to use this tool: to obtain a slug for filtering datasets in au-data-gov.dataset_search. It also notes 'no auth required,' which is a practical usage hint. However, it does not explicitly list alternatives or when not to use it, though the workflow guidance is strong enough to infer its role.

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