get_dataset_schema

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

No annotations are provided, so the description carries full burden. It discloses that the data is cached and that the cache must be generated externally, which is important behavioral context. However, it doesn't explain what happens if the cache is missing or if there are any permissions requirements.

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?

Three concise sentences, each serving a purpose: first states outputs, second emphasizes speed, third gives usage direction. No unnecessary words; front-loaded with essential information.

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 no output schema, the description adequately hints at return values (field names, example values, OPAL snippets). The parameter is fully covered. While error handling is not discussed, the tool is simple and the description is sufficient for an AI to understand its basic use.

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 single parameter dataset_path is fully described in the schema with details about default fallback and how to find paths. The description adds no additional semantic value beyond what the schema provides, so a baseline score of 3 is appropriate.

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 returns cached field names, example values, and OPAL filter snippets for a dataset. It emphasizes that it's instant and no API call is needed, distinguishing its lightweight nature from potentially heavier sibling tools like inspect_dataset.

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 explicitly advises calling this before writing any OPAL query, which defines the primary use case. It also mentions the prerequisite of generating the cache externally via the discover script. However, it does not explicitly mention when not to use this tool or compare it to alternatives like inspect_dataset.

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