spss_moderationRuns a mean-centred moderation regression (Y ~ X + W + X*W) and reports the interaction term that tests whether W moderates the X–Y relationship.
Run a mean-centred moderation regression (Y ~ X + W + X*W) and report the interaction term that tests the moderating effect of W.
| Name | Required | Description | Default |
|---|---|---|---|
| x | Yes | ||
| y | Yes | ||
| file_path | Yes | ||
| moderator | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral burden. It does disclose one meaningful trait beyond the schema: that predictors are mean-centred (which differs from a plain regression) and which interaction term it reports. However, it says nothing about data prerequisites (e.g., an SPSS .sav file, variable scale requirements) or failure behavior, leaving notable gaps for an unannotated 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?
A single, front-loaded sentence that specifies the method, the model equation, and the reported statistic with no wasted words.
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?
An output schema exists, so return values need not be explained, and the method is concisely specified. But with zero annotations and 0% parameter description coverage, the definition omits prerequisites such as the expected file type/path semantics and any data requirements, leaving it only minimally complete for a four-required-parameter analysis tool.
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 coverage is 0%, so the description must define all four parameters. The formula maps X, W, and Y to x, moderator, and y well enough to infer roles, but file_path is never addressed and the string/variable-name nature of the inputs is left implicit. Partial compensation only.
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 specific verb (run), a specific model (mean-centred moderation regression Y ~ X + W + X*W), and the exact output of interest (the interaction term testing W's moderating effect). This clearly distinguishes it from siblings like spss_regression and spss_mediation.
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 statistical formulation implicitly signals when to use it (testing whether W moderates X→Y), but there is no explicit when-to-use vs when-not guidance and no named alternative such as spss_mediation for indirect-effect questions. Usage is inferable only from the method name and formula.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.