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

Annotations already declare readOnlyHint=true and destructiveHint=false; description adds that it 'calls an LLM, no other side effects' and provides quota details. This goes beyond structured annotations by clarifying runtime behavior and consumption costs. No contradiction.

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 sentences, each with a distinct purpose: output definition, usage guidance with alternative, and side-effect/quota disclosure. No unnecessary words.

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 two parameters, no output schema, and strong annotations, the description covers purpose, usage, side effects, and quota. It tells the agent exactly when to use it, what it returns, and that it's read-only – sufficient for invocation decisions.

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?

Input schema has 100% description coverage, with 'question' and 'model' fully described. The tool description adds no parameter-specific information beyond the schema, so baseline 3 applies.

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?

Description states it 'Returns a synthesized natural-language answer with citations, grounded in the AlgoVault knowledge bundle' – a specific verb, resource, and output. It distinguishes from sibling search_knowledge by noting LLM synthesis, and from other siblings by focusing on knowledge Q&A.

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?

Explicitly says 'Use when you need an explanation, code pattern, or how-to' and contrasts with search_knowledge: 'for raw ranked snippets without LLM synthesis use search_knowledge (faster, no quota cost)'. This provides clear when-to-use and an explicit alternative.

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