web_research_tool

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. The description adds two pieces of real context beyond that: results are synthesized rather than raw, and the backing provider is Perplexity, implying an external AI call. It still omits cost, latency, result length, and any rate-limit behavior.

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 lead sentences front-load purpose and return type, followed by a compact Args block and a short provider note. The docstring formatting and bracketed provider line are slightly awkward but nothing is redundant.

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 two-parameter read-only tool with no output schema, the definition covers purpose, return type, and both parameters adequately. It leaves gaps an agent would want before calling: expected response size, whether one query covers one topic only, and any latency or cost implications of the external Perplexity call.

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 0% schema description coverage, the description carries the full burden and does so reasonably: it explains query with a concrete example and supplies the allowed values for research_type, which the schema does not enumerate as an enum. It does not explain what each research_type actually changes in the results, which keeps it below a 5.

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?

"Research a topic using AI-powered web search" gives a specific verb (research) and resource (web), and "Returns synthesized findings" states the output shape rather than raw links. It never names or contrasts a sibling such as keyword_research or brand_visibility, so an agent must infer differentiation itself.

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?

Usage is only implied: the research_type options (news, brand_research, competitor_audit) hint at when each mode applies, and the query example suggests open-ended topic research. There is no explicit statement of when to prefer this over a dedicated keyword or brand tool, and no exclusions or prerequisites.

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