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

Annotations cover the safety profile (readOnly, openWorld, non-destructive), and the description adds genuinely non-obvious behavior: a cost of 10 prepaid credits per successful call, that source links may be Google redirect URLs rather than canonical ones, and that the MCP layer returns a compacted view of results. These are real operational caveats an agent would otherwise not know. Minor gaps remain (failure/error behavior, latency) but the added context is substantial.

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 tight sentences that lead with what is returned, then cover targeting, cost, and the source-link/compact-view caveats. Every clause carries information; phrasing like "JSON text blocks" and "compact view" is slightly jargon-heavy but still load-bearing.

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, so the description must characterize returns — and it does, naming the answer text blocks, source references, and the compaction caveat. Cost and redirect-link behavior round out what an agent needs before calling a paid, open-world tool; only failure modes are unaddressed.

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% and both enum parameters (country, language) are self-documenting, establishing a baseline of 3. The description restates that country and language target the answer and that queries are natural-language, but adds no format guidance, defaults, or interaction rules 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+resource: it returns Google AI Mode answers as JSON text blocks plus source references, and describes itself as a research/Q&A lookup rather than a raw SERP fetcher. It stops short of explicitly contrasting with sibling search_web, which is the nearest alternative, so the differentiation is inferable but not spelled out.

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?

"Supports natural-language search questions with country and language targeting for research summaries" implies the intended use case (researcher seeking a synthesized answer). There is no explicit when-to-use vs when-not, and no mention of siblings like search_web or search_news, leaving the agent to infer when an AI answer beats raw search results.

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