llm_query

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

No annotations are provided, so the description carries full burden. It discloses the routing behavior, auto-detection of complexity from prompt length, and the model override. It does not mention rate limits or authorization, but as a query tool, it is likely read-only.

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 concise: a one-sentence overview followed by a clear bullet list of parameters. Every sentence adds value without redundancy.

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?

With 7 parameters and an output schema, the description covers all parameters adequately. The behavior and routing are well explained. Slightly more detail on return values could be added, but the output schema likely covers that.

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 0%, so the description must add meaning. It explains all 7 parameters: prompt, complexity (with auto-detect logic), model (override), system_prompt, temperature, max_tokens, and context. This adds significant value beyond the bare 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 clearly states 'Send a general query to the best available LLM' and explains the routing by complexity. It distinguishes itself from specialized siblings (e.g., llm_code, llm_image) by being the general-purpose query 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?

The description explains when to use (general queries) and details the complexity parameter that drives model selection. It also notes the model override. However, it does not explicitly list when not to use or suggest alternatives among the many specialized llm_* tools.

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