extract_contacts

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

With no annotations, the description carries the full burden. It discloses meaningful behavior: results are 'verified', extraction follows links into contact/about subpages, and it returns compact structured data rather than raw DOM. It omits failure modes, JS-rendered page handling, timeouts, and whether any auth or rate limits apply.

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, front-loaded with the verb and extracted resource before any justification. No filler; the second sentence functions as a routing signal rather than padding.

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?

There is no output schema and no annotations, so the description does well by naming the returned field categories. It is nearly complete for a two-parameter read tool, with only minor gaps around output shape and behavior on dynamic sites.

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 description coverage is 100%, so both 'url' and 'deep_scan' are already documented, including the subpage examples and the default. The description only restates that subpages are scanned and adds no syntax or format detail beyond the schema, so the baseline of 3 applies.

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?

States a specific verb (extract), a specific resource (contact information), and enumerates the exact output types (emails, phone numbers, WhatsApp links, social profiles). This enumeration clearly separates it from siblings like extract_web_content and extract_structured_data, which target generic or arbitrary content.

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 closing sentence implies a use case by contrasting the tool with 'feeding the entire raw DOM to the LLM', which hints at when to prefer it. However, no sibling tools are named, no prerequisites or exclusions are given, and nothing tells the agent when NOT to use it (e.g. when only page text is needed).

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