search_scrapers

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

Annotations only cover readOnly/openWorld, so the description adds real value by disclosing the return shape (dataset_id, name, domain, collection_methods) even though no output schema exists. It also defines the ordering with a downstream tool, giving the agent a usable mental model of the lookup-then-detail flow.

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 tight sentences, front-loaded with purpose, then output fields, then next action. No filler or redundant restatement of the name.

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 supplies the return fields and the recommended follow-up call, which is what an agent needs to proceed. The only gap is the unaddressed 'limit' parameter, a minor omission for a search tool.

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 0%, so the description must carry parameter meaning. It partially does so for 'query' via illustrative examples, but says nothing about 'limit' (default 10, max 50), leaving one of two parameters entirely undocumented. Partial compensation merits a mid score.

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+resource ('Find a Bright Data pre-built scraper') with scope detail (Web Scraper API, 1,200+ sites) and concrete query examples like "amazon reviews" and "linkedin.com". This is clearly distinguishable from siblings such as search_dataset and get_scraper_details.

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?

Gives example query forms and an explicit next-step workflow ('Next: call get_scraper_details with a dataset_id and one method'), which cleanly chains into the follow-up tool. It stops short of naming when NOT to use it or contrasting directly with search_dataset, so no explicit exclusions.

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