llm_groq

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

With no annotations, the description carries full burden for behavioral disclosure. It only states the action without mentioning side effects (e.g., cost, API key requirements, or response format).

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 a single, front-loaded sentence, which is concise. However, it is too brief for the tool's complexity (3 params, no schema descriptions), lacking necessary details.

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?

Despite having 3 parameters, no output schema, no schema descriptions, and no annotations, the description only provides a minimal purpose. It fails to cover parameter semantics, usage context, or return value.

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% for 3 parameters. The description only implies the 'prompt' parameter via the word 'prompt' but fails to explain 'model' or 'max_tokens', adding little value 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?

The description 'Send prompt to groq LLM' clearly identifies the verb (send) and resource (groq LLM), and distinguishes it from sibling tools like llm_anthropic or llm_openai by naming the specific provider.

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 provides no guidance on when to use this tool over alternatives from the sibling list (e.g., llm_openai, llm_gemini). An agent must infer usage solely from the tool name.

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