extract_with_prompt

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

With no annotations, the description fully discloses behavior: uses LLM, costs one call, returns JSON Lines, requires specific environment variables, optional browser flag. Does not mention error handling, but sufficient for selection.

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?

Concise and well-structured: first sentence states purpose, then configuration details, then optional parameters. No fluff.

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 covers return format (JSON Lines), cost, and all prerequisites. It is complete for an understanding of tool capabilities and requirements.

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 100%, but description adds value beyond schemas: model parameter includes override explanation and API key clarification; browser parameter adds 'auto-spawns system Chrome'; prompt parameter includes example. Justifies score above baseline 3.

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 extracts structured data using natural-language instructions, distinguishing it from sibling 'extract' which uses a CSS schema. The verb 'Extract' and resource 'URL with an LLM' are specific.

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?

Explicitly describes when to use (natural-language extraction) and provides configuration prerequisites (LLM setup). Mentions optional model override. Lacks explicit when-not-to-use for siblings like extract_inferred, but still clear.

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