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

Annotations already declare readOnly, idempotent, openWorld, and non-destructive, so the safety profile is covered. The description adds meaningful context beyond that: results are web-sourced via 'Tavily API' and synthesized with 'grounded' (i.e., source-attributed) content. It still omits latency, caching, rate limits, and failure behavior for an open-world search.

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?

A single front-loaded sentence with no filler; the action and scope come first. It is efficient, though tightly packed ('grounded synthesis') at the cost of some clarity.

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 an output schema present, return values need no explanation, and the rich annotations cover the safety profile. What remains missing is usage context — when this tool should be chosen over its seven siblings — which is a real gap for a multi-tool agent workflow.

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 parameters are already documented, making 3 the baseline. The description's mention of 'tech stack' and 'engineering culture' loosely echoes the focus_areas examples (tech_stack, culture) but adds no format or constraint detail beyond the schema.

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 ('Search web') and resource ('verified company information, tech stack, and engineering culture'), which is clear enough to act on. It does not name or differentiate itself from any sibling tool (jd_analyzer, evidence_validator, etc.), so sibling disambiguation is left to inference.

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?

There is no explicit when-to-use, when-not-to-use, or alternative-tool guidance. An agent must infer the research/pre-interview context from the name alone, and nothing tells it whether this tool supersedes or complements siblings like github_proofer or evidence_validator.

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