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Spss Manova

spss_manova

Run multivariate analysis of variance (MANOVA) for multiple dependent variables, testing multivariate effects and providing univariate follow-ups. Requires IBM SPSS Statistics.

Instructions

Run multivariate analysis of variance (MANOVA) for multiple dependent variables. Tests multivariate effects and provides univariate follow-ups. Requires IBM SPSS Statistics to be installed.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "file_path": "<file_path>",
      "dependents": "<dependents>",
      "factors": "<factors>"
    },
    "name": "spss_manova"
  }
}

Input Schema

Name	Required	Default
`method`	No	SSTYPE3
`factors`	Yes
`file_path`	Yes
`covariates`	No
`dependents`	Yes
`factor_ranges`	No
`print_univariate`	No
`print_multivariate`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 12, 2026

TDQS

C2.7/5.0

Behavior2/5

Does 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 discloses only the SPSS installation requirement and that both multivariate effects and univariate follow-ups are produced; it omits read-only vs. mutating nature, any permission or resource constraints, and runtime behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three short sentences with the core purpose front-loaded and no filler. It is efficient, though slightly under-specified rather than truly concise in the informative sense.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

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, but for an 8-parameter statistical tool with zero schema description coverage and no annotations the description is far too thin. Critical inputs like method, factors, covariates and range specifications are left entirely undocumented.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% across 8 parameters, and the description explains none of them. Nothing is said about method (SSTYPE1-4), factors, covariates, factor_ranges, or the print_univariate/print_multivariate toggles, so the agent must guess from parameter names alone.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (Run) and resource (multivariate analysis of variance / MANOVA) plus the key scope qualifier: multiple dependent variables. This implicitly separates it from spss_anova (single DV), but it never names that sibling or the several other ANOVA-family tools, so the distinction must be inferred.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives a prerequisite (SPSS must be installed) but no when-to-use criteria and no alternatives. With spss_anova, spss_glm_univariate and spss_repeated_measures_anova as siblings, the agent gets no guidance on which to pick for a given data shape.

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

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