invoke_llm

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

No annotations are present, so the description carries full burden. It only states 'generate text based on input instructions', omitting any behavioral traits such as system prompt, context, personality, cost implications, or rate limits. This is insufficient for an LLM invocation 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 a single sentence of 15 words, front-loaded with the verb and resource. Every word contributes to the core purpose, and there is no redundant information.

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 only 2 parameters and no output schema, the description lacks details on return format, error behavior, or model-specific constraints. It does not mention what happens with the generated text (e.g., streaming, token limits). The completeness is insufficient for an agent to use this tool reliably.

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 coverage is 100%, so baseline is 3. The description adds minimal value: it reiterates that input instructions are used for generation but does not elaborate on the parameters beyond what the schema already provides (e.g., not explaining the 'auto' default or model selection strategy).

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 verb 'invoke' and the resource 'language model', with the action 'generate text based on input instructions'. It distinguishes itself from many sibling tools that are domain-specific (e.g., company research, file conversion) by indicating it is a generic LLM invocation tool.

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 other LLM-related tools like openclaw_chat_completion or invoke_agent. There is no mention of context, prerequisites, or situations where one alternative should be preferred over another.

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