List datasets

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

Annotations already state readOnlyHint=true and openWorldHint=true, so the description adds value by disclosing the catalogue size (100 datasets), the return type (names and titles), and the hiding of discontinued datasets unless requested. No contradiction with annotations; this extra context helps an agent understand the tool's behavior beyond the safety hints.

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 wasted words. It front-loads the core purpose, then the return type, a necessary next step, and a behavioral note—all essential information. This is a model of concise, effective tool documentation.

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 discovery tool with 0 required parameters and no output schema, the description covers the key usage points: what it returns, how to proceed, and a special behavior (discontinued). It doesn't detail pagination or limit semantics, but those are present in the input schema, so the description is complete enough for correct invocation.

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 75%, and the description does not add significant meaning beyond what the schema already provides. It mentions 'search' and 'discontinued' indirectly, but those are already described in the schema. The baseline of 3 applies because the schema carries the parameter semantics.

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 ('Search'), a resource ('Energi Data Service catalogue'), and scope ('Danish electricity, gas and grid data'). It also distinguishes itself from siblings by saying it returns dataset names and titles and pointing to describe_dataset for next steps, making it clear this is the metadata listing entry point.

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: it returns dataset names and titles and instructs to call describe_dataset before querying one, which implies the proper workflow. It doesn't explicitly exclude alternatives like query_dataset, but the guidance is sufficient to know when to use this tool as a discovery step.

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