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

With no annotations, the description carries the full burden. It discloses the model family, output artifacts like percentiles and confidence intervals, and the $0.015 per-call cost. It does not mention failure modes or whether repeated calls are deterministic, but for a prediction tool it provides substantial transparency.

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 well-structured and front-loaded with the core purpose, followed by model details, outputs, cost, and use-case trigger. The phrase 'complete mathematical transparency' is slightly redundant with the listed outputs, but overall every section earns its place.

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?

The description covers purpose, model selection, outputs, cost, and when-to-use, which is solid for a simulation tool. However, it does not compare itself to the sibling tool card_forecast, and it omits guidance on the simulations parameter, leaving meaningful gaps for an agent trying to call the 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 0%, so the description must compensate. It explains the 'model' parameter well by naming 'gbm' and 'merton' options and the conformal default, but it leaves card_name, current_price, days, and especially simulations semantically unexplained. The simulations parameter, with a default of 20000, is a significant gap.

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 a specific verb and resource ('Predict future trading card value') and provides rich detail about the model and outputs. However, it does not differentiate itself from the sibling tool card_forecast, which likely overlaps in purpose, so it stops short of a 5.

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 'Use this when' section gives explicit triggering examples: users asking about card value in 3 months or wanting price trajectory predictions. It gives clear usage context but does not mention alternatives or when not to use this tool, so it earns a 4 rather than a 5.

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