create_dataset

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

No annotations are present, so the description carries the full burden—and it delivers: it discloses asynchronous execution (job id + polling), output formats, billing per found record, premium field surcharges, and a hard cost cap with refund. This is strong behavioral disclosure for a complex, potentially expensive tool.

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?

Two dense sentences are front-loaded with the core purpose, followed by the async contract and billing rules. No filler or repetition of schema content; every clause 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 complex 6-parameter tool with no annotations and no output schema, the description explains the pipeline, the job-id/polling flow, output formats, and the full billing and cost-cap behavior. An agent has enough context to invoke it correctly and set user expectations.

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 83%, so the schema already documents most parameters (prompt, limits, columns, sources, country, webhook). The description adds meaning mainly to limits.max_cost_usd by explaining the budget cap and refund behavior, but does not materially enrich the other parameters.

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?

States a specific verb ('Build') and resource ('structured dataset'), and explains the full pipeline from prompt to validated rows in CSV/JSON. It also distinguishes itself by naming the polling endpoint (dataset_status), which separates it from direct scrape/crawl siblings.

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 implies the intended use case: converting a plain-language request into a structured, validated dataset via multi-engine search and scraping. It does not explicitly name alternatives or when-not-to-use, but the scope is specific enough for an agent to select it over raw scrape/crawl tools.

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