multi_llm_query

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

No annotations exist, so the description carries the full burden, and it does disclose meaningful behavior: execution is parallel, failures are isolated per engine ('Engines that error are reported per-engine and don't block the others'), and results depend on which credentials are configured. It omits cost/latency implications of fanning out to six providers, which matters for an agent deciding whether to call it.

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?

Three tight blocks with zero waste. The core action is front-loaded, the parameter detail follows, and the failure-isolation note is last — exactly the order an agent needs.

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?

An output schema exists, so return values need not be explained. Given only two parameters, the description covers the non-obvious one exhaustively, explains the default engine set, names where to check credentials, and states partial-failure semantics — everything needed to call it correctly.

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% and the `engines` property is an unconstrained string array, so the description is the only source of the valid values; it supplies the full list ["perplexity","openai","anthropic","gemini","grok","brave"] plus the null/'all configured' default. `question` is left undescribed but is self-evident from the required-string schema and the opening sentence.

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?

States a specific verb and resource ('ask the same question to all (or selected) configured LLM engines in parallel'), which inherently separates it from the single-engine siblings like perplexity_query, openai_query, claude_query and gemini_query. It does not, however, distinguish itself from the similarly named sibling multi_llm_citation_check, which an agent could easily confuse with it.

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?

Gives concrete operating context: `engines` is an optional subset, the default is every engine with credentials, and the agent is pointed at geoseo_status to discover which engines are configured. There is no explicit 'when not to use this' or a pointer to the single-engine siblings, so it stops short of full routing guidance.

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