Extract structured data

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

Annotations already indicate readOnlyHint=false and openWorldHint=true. The description adds behavioral nuance by disclosing the async job nature ('Async job — poll crw_check_extract_status with the returned id') and a key prerequisite ('Needs an LLM'). This provides context beyond the annotations, such as the non-blocking execution model and the requirement to track progress via a returned identifier.

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?

The description is exactly two sentences: the first states the core purpose and method, the second covers the async workflow and prerequisite. Every clause adds value, and the most important information is front-loaded. There is no redundancy or filler.

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 tool with 7 parameters, nested objects, and an output schema, the description efficiently covers the essential workflow (async, polling, LLM requirement). The existence of an output schema means return values are documented elsewhere. It does not discuss error handling or rate limits, but these are less critical given the asynchronous pattern and available schema documentation. Overall, it is complete enough for an agent to select and invoke the tool correctly.

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 schema covers 71% of parameters with descriptions, so the baseline is 3. The description adds meaningful semantics by explaining that extraction works 'via a prompt and/or JSON schema', clarifying the relationship between prompt and schema parameters. It also highlights the LLM dependency, tying together llmModel/llmProvider/llmApiKey even though the schema doesn't explicitly state they are required. This goes beyond the schema's bare parameter list.

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 begins with 'Extract structured JSON from URLs via a prompt and/or JSON schema', which clearly states the verb ('extract'), the resource ('URLs'), and the output format ('structured JSON'). It also distinguishes from sibling tools like crw_scrape (raw scraping) and crw_map by emphasizing structured extraction. The async note differentiates it from synchronous tools.

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 provides context that it is an async job and must be polled via crw_check_extract_status, and that it needs an LLM. However, it does not explicitly state when to use this tool over alternatives such as crw_scrape or crw_crawl, nor any exclusions. The usage context is clear but lacks explicit when-to-use/when-not-to-use guidance.

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