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

Beyond the annotations (readOnlyHint, openWorldHint, destructiveHint), the description adds critical behavior: name resolution reporting, exact price reproduction, warning propagation, and distinguishing missing columns from zero-cost models. No contradictions found.

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 efficient with three sentences plus an IMPORTANT instruction. It is front-loaded with the primary purpose and uses a natural flow. Slightly more structured (e.g., bullet points) could improve readability, but it is well within acceptable bounds.

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 that an output schema exists (so return values are covered), the description covers purpose, usage boundaries, resolution behavior, and important agent instructions. No obvious gaps for the tool's complexity level.

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 description coverage is 100% already. The description adds meaningful context about model resolution behavior and volume presets, but does not significantly deepen parameter explanations since the schema already defines them well.

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 compares 2-4 named LLM models against all 8 use-case profiles at a chosen monthly volume. It distinguishes itself from siblings by explicitly contrasting with filtering the whole catalogue and referencing estimate-llm-cost as an alternative.

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 when to use ('when the user names specific models to weigh against each other') and when not to use ('if they also supply their own token counts, or a volume outside 10k/100k/1m, use estimate-llm-cost'). Provides clear decision boundaries.

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