AI Search

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

Annotations already declare readOnlyHint=true, openWorldHint=true, and destructiveHint=false, so the safety profile is covered upstream. The description adds nothing behavioral beyond that — no mention of source coverage, latency, billing, or how results differ between models/sources. It essentially restates the annotation's open-world nature without new information.

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 zero padding or repetition — structurally clean. It avoids wasting tokens, though its brevity borders on under-specification rather than true conciseness.

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?

For a 9-parameter, open-world search tool with no output schema, the description should at minimum hint at what comes back and how source/model selection shapes results. It supplies none of that, leaving the agent reliant entirely on the schema to understand a fairly complex 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?

Schema description coverage is 100%, so all nine parameters (model, tools, date filters, result_type, domain filters) are already documented in the schema with defaults, enums, and examples. The description adds no parameter meaning whatsoever, so the baseline 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?

"AI search and analysis on web using Desearch AI" gives a verb (search/analyze) and a resource (web), so the general purpose is inferable. But against siblings like web-search, x-search, and extract, it offers no distinguishing scope — it does not say what makes this an "AI" search versus the plain web-search tool. The purpose is vague enough that an agent couldn't confidently route between them.

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 when-to-use guidance, no when-not-to-use, and no named alternative. With 14 siblings including web-search and x-search, an agent has no signal for choosing this tool over them. The description is purely a label, not usage instruction.

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