extract_data

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

With no annotations provided, the description carries the full burden of disclosing behavioral traits. It only mentions that extraction is LLM-based but says nothing about output format, failure handling, rate limits, or whether web search changes behavior. This leaves the agent without critical operational expectations.

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 a single, focused sentence that immediately communicates the tool's primary function. No filler or redundant content, making it highly efficient and front-loaded.

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 the absence of an output schema and annotations, the description is too terse to be complete. It does not explain what structured data means in terms of return format, nor does it clarify how this tool differs from extract_with_schema. The tool's context is insufficient for an agent to use it confidently.

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 fully describes all three parameters (urls, prompt, enableWebSearch), so schema coverage is 100%. The description itself adds no additional parameter context beyond what the schema already offers, hitting the baseline 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 states a clear action ('Extract structured data from webpages') and method ('using LLM'), making the core purpose understandable. However, it does not distinguish itself from the sibling tool 'extract_with_schema', which likely has a similar purpose, leaving some ambiguity about when to pick this one.

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 no guidance on when to use this tool versus alternatives like extract_with_schema or scrape_page. There are no stated exclusions or preferred scenarios, so the agent receives no decision support.

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