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

Annotations already cover the read-only, non-destructive profile. The description adds context about geo-targeting and the cited-sources output, which is useful beyond the annotations, but it does not go deeper into response size, rate-limit behavior, or the effects of the 'include' flags.

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 deliver the core action, output, and the exact research use case. There is no filler or repetition; each clause earns its place.

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 4-parameter tool with no output schema, the description gives a solid overall picture: input prompt, geo-targeting, optional state, and the main output. It does not describe the response structure in detail, but that is mitigated by the 100% schema coverage and the fact that the output's 'cited sources' nature is stated.

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 parameter semantics are already fully documented. The description mainly restates the idea of a prompt and geo-targeting without adding meaning beyond the schema's own descriptions.

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 and resource: 'Submit a prompt to Google Gemini' — and explicitly describes the returned artifact: 'the answer with cited sources.' It names the model being scraped, which distinguishes it from sibling scrapers like scrape_chatgpt and scrape_perplexity.

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 phrase 'Use this to see how Google Gemini answers a prompt and which brands/sources it mentions' gives clear when-to-use context for brand/source research. It does not explicitly state when not to use it or name alternatives, but the model-specific framing combined with sibling names provides usable routing guidance.

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