extract_table

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

Without annotations, the description fully explains header extraction logic (fallback to first row's <th> cells) and row mapping (keyed by header or 'col_N'), providing clear behavioral transparency beyond the tool name.

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 efficiently convey purpose, behavior, and use cases without wasted words, front-loading the core action and output.

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?

Despite lacking an output schema, the description specifies the return structure (headers, rows, row_count) and covers edge cases (missing <thead>, missing header names), making it complete for a single-parameter 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?

With 100% schema description coverage for the single parameter 'selector', the description does not add extra detail about the selector, but the schema already provides adequate meaning, warranting a baseline score of 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 it extracts a table into {headers, rows, row_count} using a CSS selector, and provides specific use cases like pricing tables and specs, distinguishing it from sibling extraction 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?

It explicitly recommends the tool for table extraction and emphasizes the saved effort of manual mapping, but does not mention when not to use it or list alternative tools for non-table structures.

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