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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, so the safety profile is covered. The description adds significant behavioral context beyond annotations: keyless callers get a top-3 preview while live keys return up to 25, the response includes baseline vs scenario composite plus component breakdown, and the exact formula/weights are returned (deliberately NOT the DCPI). This is rich, non-redundant behavioral disclosure.

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 dense but efficiently packed. It front-loads the core purpose and then systematically covers parameters, return behavior, exclusions, and an example. It is somewhat long as a single paragraph, but every sentence contributes actionable information with no filler.

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?

Although an output schema exists, the description still explains what the response contains (baseline vs scenario composite, component breakdown, formula/weights) and the keyless vs keyed access difference. Combined with the explicit usage boundaries and parameter rules, an agent has everything needed to call this tool correctly.

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 value by noting 'all optional, pass at least one delta', clarifying the ranking criterion ('ranked by |score change|'), giving concrete examples for parameters (e.g., 'avg_kwh_cents_pct=30'), and explaining market slugs come from rank_markets. This goes beyond the schema's per-parameter descriptions.

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 opens with 'Counterfactual WHAT-IF re-scoring of 300+ DC Hub power markets under YOUR explicit deltas' — a specific verb ('re-scoring'), a specific resource ('DC Hub power markets'), and a clear conditional ('if conditions change'). It also explicitly distinguishes itself from siblings like rank_markets and predict_market_trajectory, making selection unambiguous.

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 explicitly states when to use this tool ('answers explicit hypotheticals') and when not to: 'Do NOT use for the present-day ranking (use rank_markets) or trajectory extrapolation (use predict_market_trajectory)'. It also provides a concrete example invocation, leaving no ambiguity about tool selection.

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