get_forecast

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

With no annotations provided, the description carries the full burden. It discloses that the forecast is distribution-free, deterministic, never-under-protective, and that risk values are honest out-of-sample. It also mentions 'two plain-English letter grades.' However, it could be more explicit about side effects or return behavior beyond the grade mention.

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 concise (~100 words) and front-loaded with the main purpose. Every sentence adds value, distinguishing the tool and its methodology without waste.

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 one parameter and the existence of an output schema, the description is fairly complete. It explains methodology and key traits, but could briefly mention what the output schema covers to enhance completeness.

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 has 100% description coverage for the single parameter 'card_name'. The description does not add additional meaning beyond the schema's example. Baseline score is appropriate.

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 'Get the calibrated conformal risk forecast for a trading card.' This is a specific verb+resource. It distinguishes from siblings like 'simulate_price' by explicitly contrasting with Monte Carlo simulation and positioning itself as the 'honest default forecast.'

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 implied usage guidance by calling it the 'recommended, honest default forecast' and contrasting with Monte Carlo simulation. It explains why it's distribution-free and never-under-protective, helping the agent understand when to prefer this tool, but does not explicitly state when not to use it or name specific alternatives.

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