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

Annotations already provide readOnlyHint, idempotentHint, and destructiveHint, but the description adds substantial behavioral depth: confidence classes are 'never blended', the derived number counts only under-construction+testing minus retirements, speculative MW is 'shown but never folded in', and the tool declares out-of-coverage items rather than estimating them. This goes well beyond the annotations and sets honest expectations.

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 verbose but information-dense; every sentence earns its place by adding source-composition detail, derived-number semantics, example queries, or exclusions. It is front-loaded with the core purpose and ends with an honesty line. The length is justified by the tool's compositional complexity, though it could be tightened.

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 the annotations, a 100%-covered input schema, and an output schema, the description is complete: it specifies scope, data sources, derived-number logic, confidence-class handling, exclusions, and honesty constraints. An agent has everything needed to invoke the tool correctly and interpret the result appropriately.

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%, so the baseline is 3. The description adds some contextual meaning around 'year by year' and state-level timing, but the parameter descriptions in the schema already explain state, years, and mw's context-only behavior. The description does not materially improve parameter understanding beyond the schema.

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 states a specific verb, resource, and scope: 'Power-availability TIMING for one US state — when power gets EASIER, year by year.' It clearly differentiates from siblings with the explicit 'Do NOT use for' list naming get_power_pipeline, get_grid_intelligence, get_refined_queue, rank_markets, and ai_capacity_index. This is a resourced, scoped, and well-differentiated purpose statement.

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 gives explicit when-to-use guidance with concrete example questions ('when is new capacity landing in Ohio', 'what comes online in Georgia by 2027') and explicit when-not-to-use alternatives via the 'Do NOT use for' segment. It also clarifies the tool's scope boundary: it answers WHEN, for one state, not queue depth or ranking.

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