fluig_dataset_structure

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

Discloses safe, read-only behavior ('WITHOUT running it'), which is critical for an AI agent. No annotations, so description carries full burden; it adequately covers the non-destructive nature.

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?

Single, front-loaded sentence with no unnecessary words. Every phrase 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?

Given the simplicity of the tool (one parameter, no output schema), the description fully covers purpose, behavior, and usage context. No gaps remain.

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 coverage is 100% with a basic description ('Dataset id.'). The tool description does not add further parameter semantics beyond what the schema already provides.

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?

Explicit verb 'Return' and precise resource 'dataset's structure (columns and types)'. Distinguishes from sibling fluig_dataset_run by specifying 'WITHOUT running it' and states usefulness for composing queries.

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?

Clearly indicates when to use (before composing a query to learn schema). Does not explicitly exclude alternatives like fluig_dataset_run, but context is strong enough for an agent to infer.

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