Extract Structured Data

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

Adds genuine context beyond the annotations: extraction runs through MCP sampling on the caller's model, and there is no extra Bright Data cost beyond the page fetch. It also implicitly signals caching via the fresh parameter's cost note. It doesn't disclose failure behavior when the model returns schema-invalid JSON, which is the main remaining gap.

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 sentences, no waste; the mechanism and cost note come first and the parameter call-to-action second. Every clause carries information an agent needs.

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?

With no output schema, the description must convey the return shape and does (JSON, optionally constrained by a provided schema), and the cost model is covered. Missing only edge-case behavior such as what happens on extraction or schema-validation failure.

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 80%, so most parameters are already documented in-schema (fresh, country, schema, extraction_prompt). The description reinforces that extraction_prompt is the core input and schema is optional shaping, but adds no syntax or format detail beyond the schema. Baseline 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?

States a specific verb ('Fetch a page') and transformation ('turn it into JSON') plus the non-obvious mechanism (MCP sampling with the caller's own model). This distinguishes it functionally from scrape_as_markdown/scrape_as_html, though it never names those siblings, so an agent must infer the boundary.

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 implies when to use it (when you want structured JSON shaped by a prompt/schema) and notes the cost profile, but gives no explicit when-to-use/when-not guidance relative to scrape_as_markdown or scrape_as_html. The choice must be inferred from the output format implied by each tool name.

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