Validate sampler health and model fit by checking posterior predictive coverage, prediction errors, and residual behavior before running any marketing decisions.
Mandatory diagnostic gate. Checks sampler health plus posterior predictive coverage, predictive error, and residual behavior before decision tools may run.
| Name | Required | Description | Default |
|---|---|---|---|
| model_id | Yes |
Changes observed during successful MCP inspections.
v0.4.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses the categories of diagnostics performed and the gating behavior, but it does not say what happens when diagnostics fail, whether the operation is read-only, or what the return value looks like. This is a moderate gap for a gating tool.
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 a single, efficient sentence with no filler. The most important information ('Mandatory diagnostic gate') is front-loaded, and every clause adds specific value.
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?
For a one-parameter tool, the description covers its purpose and placement well. However, it lacks any information about output format, failure semantics, or how results should inform next steps. Given the absence of annotations and output schema, the description is not 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 description coverage is 0% and the description never mentions model_id. The parameter name is somewhat self-explanatory, but the description adds no meaning beyond the schema field name, so it only minimally compensates for the coverage 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 states a specific verb ('Checks') and a precise resource (sampler health, posterior predictive coverage, predictive error, residual behavior), and frames the tool as a 'Mandatory diagnostic gate' that precedes decision tools. This clearly differentiates it from the many prediction and optimization siblings.
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 explicitly says this runs 'before decision tools may run,' giving clear when-to-use context. It does not name specific alternative tools or exclusions, but the mandatory-gate framing strongly signals its required position in the workflow.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.