neuron_extract_data

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

There are no annotations, so the description carries the behavioral disclosure burden. It discloses key behaviors: selector-based extraction, auto-detection, the types of data extracted (text, links, images, data attributes), and the 200-item limit. It does not explicitly state side-effect-free behavior, but 'extract' strongly implies a read-only operation.

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 three sentences with no wasted words. It front-loads the core purpose, explains the selector behavior, and ends with the output limit. Each sentence contributes meaningful guidance for invocation.

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 provides enough about return content by listing text, links, images, data attributes, and the 200-item cap. It could be more complete by describing the exact return shape or field names, but for an extraction tool with two parameters, this is a solid level of completeness.

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 input schema has 100% description coverage for both parameters, so the description needs to add limited parameter-specific value. It does clarify that selector is optional and controls whether extraction targets specific elements or auto-detects patterns, which slightly enriches the schema. However, it does not add major detail beyond the schema's own descriptions.

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 what the tool does: extracts structured data from repeating page patterns, with explicit examples like product listings and search results. It distinguishes its auto-detection behavior from selector-based extraction, but does not explicitly differentiate itself from sibling tools such as neuron_find_elements or neuron_extract_to_json.

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 clear usage context, indicating the tool is for repeating page patterns and explaining the conditional behavior when a CSS selector is provided versus when auto-detection is used. It does not explicitly mention when to prefer an alternative tool, but the context is sufficient for an agent to recognize the intended use case.

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