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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds behavioral context by describing the output components (percentile rank, trend, confidence, source_lineage, methodology_version), the data source (EIA), update frequency ('Updates: weekly'), and the historical depth ('history since 1975'). This goes beyond the annotations and is consistent with them.

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 two sentences long: the first conveys the metric's definition and output fields, the second provides usage guidance and update frequency. Every clause is informational, with no filler or repetition. It is dense but readable, earning a top score for conciseness.

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 no output schema, the description compensates by listing the return fields (score, percentile rank, trend, confidence, source_lineage, methodology_version) and data source. It also includes usage triggers and update frequency. While it doesn't detail the format of every field or the 'days' parameter behavior, that parameter is fully covered by the schema, so the description is sufficiently complete for its complexity.

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?

The schema fully documents the only parameter ('days') with a description covering optionality, range, and tier requirements (100% coverage). The description does not add any parameter-specific information, so it doesn't improve on the schema. Baseline 3 is appropriate since schema handles parameter semantics completely.

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: 'Returns a 0-100 renewable-capacity growth momentum score' with specific components like percentile rank, trend, confidence, and methodology. The verb 'Returns' and resource 'renewable-capacity growth' are explicit, and the detailed output list distinguishes it from sibling tools that likely cover other metrics.

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 provides explicit trigger scenarios: 'Call when the user asks about renewable capacity growth, clean-energy buildout, solar/wind expansion, or energy transition pace, or when timing renewable infrastructure investment screening...' This is clear context for when to use the tool. However, it lacks explicit when-not-to-use guidance or references to alternative sibling tools, so it doesn't fully meet the top criterion.

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