llm_gemini

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

With no annotations, the description carries the full burden. It only states the basic action (sending a prompt) without disclosing behavior like response format, authentication needs, rate limits, or cost implications.

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 very concise at one sentence, but it is under-specified. It could include more details without losing brevity, especially given the two parameters.

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 large set of sibling LLM tools, the description is incomplete. It does not explain what makes Gemini unique, what the response contains, or any usage context, leaving the agent without enough information to choose or invoke correctly.

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 0%, meaning the schema provides no documentation. The description adds no information about the 'prompt' or 'model' parameters beyond their names and types. It fails to compensate 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 'Send prompt to Google Gemini' clearly states the tool's action (send prompt) and the specific resource (Google Gemini), which distinguishes it from sibling LLM tools for other providers like Anthropic or OpenAI.

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?

No guidance is provided on when to use this tool versus alternatives such as llm_anthropic, llm_openai, etc. The description does not mention scenarios, pros/cons, or exclusions.

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