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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, and the description is fully consistent with those. Beyond annotations, it discloses rich behavioral context: it returns baseline vs. scenario composite, component breakdown, and 'the EXACT formula/weights in every response,' plus the keyless preview limitation and the live-key behavior (up to 25). It even clarifies the composite is 'deliberately NOT the DCPI,' adding transparency about what the result is not.

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 long but every sentence earns its place. It front-loads the core purpose, then compresses parameter semantics, output contents, access tiers, example usage, and exclusions into a compact block. There is no fluff or repetition of schema descriptions — only additive guidance.

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?

For a tool with 7 optional parameters, no required params, and rich annotations, the description covers all decision-relevant context: the delta requirement, per-market vs. top-N behavior, output structure, formula transparency, key-based access limits, and explicit sibling routing. The presence of an output schema means return values don't need enumeration, and nothing critical is missing for an agent to invoke this 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 schema already documents each parameter. The description adds meaningful usage semantics on top: all params are optional but 'pass at least one delta,' top_n is 'ranked by |score change|' with default 10 and max 25, and delta params carry intuitive example values (e.g., avg_kwh_cents_pct=30). This goes beyond restating parameter names, though the schema itself already carries most of the burden.

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 a specific verb and resource: 'Counterfactual WHAT-IF re-scoring of 300+ DC Hub power markets under YOUR explicit deltas.' It clearly states the tool's unique job and reinforces distinctiveness by noting 'only DC Hub holds the underlying components.' This differentiates it from siblings without needing to read their schemas.

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 tells the agent when NOT to use it: 'Do NOT use for the present-day ranking (use rank_markets) or trajectory extrapolation (use predict_market_trajectory); this answers explicit hypotheticals.' It also provides concrete example invocations and a try-me example ('Try: simulate_scenario avg_kwh_cents_pct=30 top_n=10'), leaving no ambiguity about when to call this tool vs. alternatives.

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