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

Annotations declare readOnlyHint=true, and the description adds valuable context: it discloses the computation method (90-day window z-scored against baseline), output fields, and update frequency ('Updates: daily'). This goes beyond annotations without contradicting them. Could improve by mentioning the Gold tier requirement for history data, but that's in the schema.

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 front-loaded with the core return value and formatted as two sentences. It packs necessary details (score range, data sources, output fields, usage cases, update frequency) without extraneous words. Every clause earns its place.

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?

No output schema exists, so the description lists the key fields returned (median, p10-floor, baseline median, z-values), plus the score. It also gives context on when to use it and data freshness. This is complete for a read-only analytics tool.

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%, with the 'days' parameter fully described in the schema. The tool description does not mention the parameter, but at this high coverage the schema carries the burden. Baseline 3 is appropriate.

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 returns a specific 0-100 AI inference cost-efficiency score, with a defined formula and data source. It uses a specific verb ('Returns') and resource ('AI inference cost-efficiency score') that distinguishes it from sibling tools, even without explicit comparison.

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?

Explicit when-to-use guidance is provided: 'Call when the user asks about LLM pricing trends, cost per token, or whether inference is getting cheaper, or when timing model-routing changes, committed-spend negotiations, or AI COGS forecasts.' However, it does not mention when not to use or alternative tools, so it misses the exclusions needed for a 5.

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