llm_gemini

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

No annotations provided, so the description bears full responsibility. It discloses non-interactive execution, subscription model, and available models. It could mention error handling or response format, but overall is sufficiently transparent for a simple routing tool.

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?

The description is concise: a header line, a paragraph, a list of models, and Args. Every sentence adds value, no fluff. Well-structured for quick parsing.

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?

Given the tool's simplicity (2 parameters, output schema exists), the description covers purpose, usage context, subscription, models, and parameter details. It lacks return value explanation, but output schema fills that gap.

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 documents both parameters in the Args section, adding meaning beyond the schema: prompt is 'The task or question to send', model has default and list of options. This fully compensates for missing schema 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?

The description clearly states the tool routes tasks to the local Gemini CLI agent, distinguishes it from siblings like llm_route (which likely uses other models), and lists available models. This provides specific verb and resource.

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 description explicitly says this tool uses Google One AI Pro subscription (not Claude quota), ideal as a fallback when Claude limits are tight, or for tasks benefiting Gemini models. This provides when-to-use and when-not-to-use context.

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