llm_gemini

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

No annotations provided, so description carries full burden. It states non-interactive execution and uses the user's Google subscription, but omits important behavioral details such as whether the call is synchronous, error handling, rate limits, or timeouts. This leaves gaps for an AI agent.

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 organized in paragraphs with a clear heading and args list, but contains some redundant phrasing (e.g., 'Uses the Gemini CLI to run tasks non-interactively' repeats the purpose). Could be tightened without losing meaning.

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?

Output schema exists, so return values need no explanation. However, given no annotations, the description should cover behavioral completeness—it lacks details on sync/async, response format, and error handling. Adequate for a simple tool but not fully comprehensive.

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 adds meaningful context for both parameters: explains 'prompt' is the task/question, and 'model' lists available options with a default (gemini-2.5-flash). This compensates well for the lack of 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 it routes tasks to the local Gemini CLI agent, specifying it uses Google's subscription and runs non-interactively. It distinguishes from siblings by mentioning it's a fallback for Claude limits, which is unique among many llm_* siblings.

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?

Explicitly suggests use as a fallback when Claude limits are tight or for tasks benefiting from Gemini models. While it doesn't list when not to use, the guidance is clear enough for an AI to decide when to invoke this tool over alternatives.

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