multi_llm_query

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

With no annotations, the description discloses important behavioral traits: parallel execution, per-engine error reporting, and default engine selection. It clearly states the non-blocking nature of errors. However, it doesn't explicitly confirm that the operation is non-destructive, though that is implied.

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, comprising two sentences that front-load the main purpose and provide essential details in a structured manner. 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?

Given the tool has only two parameters (one required), an output schema, and a straightforward purpose, the description is sufficiently complete. It covers the optional engine selection, default behavior, and error handling, leaving no critical gaps.

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 compensate. It adds meaning for the 'engines' parameter by listing allowed values ("perplexity","openai","anthropic","gemini") and explaining the default. However, it does not describe the 'question' parameter beyond its name, leaving some semantics to 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 clearly states the tool's purpose: 'Ask the same question to all (or selected) configured LLM engines in parallel.' This distinguishes it from sibling tools like claude_query, gemini_query, openai_query, and perplexity_query which target single engines, and multi_llm_citation_check which has a different purpose.

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 the tool (for parallel queries to configured LLM engines) and how to optionally select engines. It mentions default behavior (every engine with credentials) and error handling. However, it doesn't explicitly state when not to use it or compare alternatives, though the context is already clear.

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