llm_anthropic

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

No annotations are provided, and the description fails to disclose any behavioral traits such as whether the call is synchronous, supports streaming, requires authentication, has rate limits, or error handling. The agent has no insight into side effects or operational characteristics.

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, short sentence, which is concise and front-loaded, but it lacks sufficient detail to be adequately informative. It is under-specified rather than efficiently complete.

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 complexity of an LLM tool (3 parameters, no annotations, no output schema), the description is severely incomplete. It does not address return values, error scenarios, or usage context, leaving the agent with minimal actionable information.

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%, and the description does not explain any parameters. While prompt, model, and max_tokens are somewhat self-explanatory, the description adds no additional meaning, especially for model values or constraints.

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 states the action (send prompt) and the specific LLM provider (Anthropic), which distinguishes it from other LLM tools for different providers. However, it is slightly vague about the exact nature of the call (e.g., chat or completion).

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 vs alternatives like llm_openai or llm_gemini. There is no mention of use cases, prerequisites, or criteria for selecting this tool.

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