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

Annotations only declare readOnlyHint=true; the description adds substantial context on top: this is a paid call settled in USDC on Base, and it will refuse with a named error code when the ledger has fewer than 30 samples. That is real behavioral disclosure an agent needs before calling. It does not clarify whether payment is still charged on refusal, which keeps it from a 5.

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 tight sentences, zero filler, and the cost/failure constraints are front-loaded where an agent will see them before committing. Every clause carries information the schema does not.

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?

There is no output schema, so the description should say what comes back — a calibrated probability value, presumably with sample count or confidence — and it does not. Combined with the unexplained task_type parameter, the definition is workable but leaves gaps an agent would have to discover by calling.

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% across 3 parameters, so the description must carry the load. It only implicitly conveys 'model' and 'prob' via 'a model's raw probability'; the optional task_type parameter is never mentioned, nor is the expected format of 'model' (name, version, provider) or how prob is interpreted.

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?

States a precise verb and transformation: 'Map a model's raw probability to an empirically calibrated one using resolved outcomes on the ledger.' An agent immediately knows the operation is a probability recalibration, not a report or label fetch. It stops short of naming how it differs from siblings like court_calibrate_labels, so it sits at 4 rather than 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 description gives a clear precondition ('refuses with insufficient_data below 30 samples') and a cost signal (paid via x402/USDC), so the agent knows the failure mode. However, it never says when to pick this over court_calibrate_labels or court_model_report, leaving sibling selection to inference.

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