Apply a frozen calibration model to independent prediction channels to adjust raw predictions into calibrated scores using supplied model and prediction objects.
Apply an explicit frozen calibration model to independent prediction channels.
| Name | Required | Description | Default |
|---|---|---|---|
| model | Yes | ||
| predictions | Yes |
Changes observed during successful MCP inspections.
v0.2.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false and closed-world operation, so the safety profile is covered. The description confirms a pure transformation but adds nothing about channel-mismatch handling, whether inputs are copied or mutated, or output shape; with annotations carrying the main burden, this is adequate but thin.
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?
A single front-loaded sentence with no filler or redundancy. It is efficient, though arguably too terse for a tool with two opaque nested-object parameters.
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?
This is a non-trivial tool: two required nested objects with zero schema descriptions and no output schema. The description does not explain what shape predictions/model must take, what a calibration model consists of, or what comes back, leaving an agent unable to construct a valid call.
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% and both parameters are free-form nested objects with additionalProperties=true, so their expected keys and structure are completely opaque. The description mentions 'model' and 'prediction channels' conceptually but adds no key names, formats, or shape guidance to compensate for the schema 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 gives a specific verb ('Apply') and resource ('an explicit frozen calibration model') plus the target ('independent prediction channels'), so the operation is identifiable. It implicitly contrasts with the sibling ap_fit_calibration (frozen vs. fitted), but never names that sibling, so the differentiation is left to inference.
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 word 'frozen' hints at the condition for use (a pre-existing, already-fit model rather than estimating one), which is weak implicit guidance. No explicit when-to-use, prerequisites, or named alternative (e.g., ap_fit_calibration for producing a model) is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.