boxleiter_estimate

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

Discloses it's a heuristic, not a forecast, includes error rate from author. Describes return structure with low/median/high brackets and calibration warning, compensating for lack of annotations.

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?

Two paragraphs plus Args/Returns section, front-loaded purpose, every sentence adds value. Efficient and well-structured.

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 output schema exists, description explains return structure in detail. All parameters documented. Tool is simple; description is fully complete.

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 has no descriptions (0% coverage), but description fully explains both parameters: review_count as total Steam reviews, price_cents as list price in cents with example (2499 for $24.99). Adds meaning beyond 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?

Clearly states it applies the Boxleiter rule-of-thumb heuristic, distinguishes from get_forecast as a sanity check. Provides specific formula and author context.

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?

Explicitly says when to use (quick heuristic) and when not (not calibrated), suggests comparing with get_forecast for outlier detection. Names sibling tool.

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