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CongJyu

spss-studio-mcp

by CongJyu

Spss Genlin

spss_genlin

Run generalized linear models with flexible distributions and link functions, including Poisson, binomial, gamma, negative binomial, using IBM SPSS Statistics.

Instructions

Run generalized linear model (GENLIN) with flexible distribution and link functions. Supports Poisson, binomial, gamma, negative binomial, and other distributions. Requires IBM SPSS Statistics to be installed.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
linkNo
scaleNo
dependentYes
file_pathYes
predictorsYes
categoricalNo
distributionNoNORMAL
save_predictedNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.9/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It usefully discloses the external dependency ('Requires IBM SPSS Statistics to be installed'), but says nothing about whether files are modified, how save_predicted behaves, or side effects. Partial behavioral context only.

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 tight sentences, front-loaded with the core action. No wasted phrasing, though the final sentence is a practical constraint rather than descriptive filler.

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?

For a tool with 8 parameters, zero schema descriptions, and no annotations, the description is too thin — the agent gets no help mapping its arguments. The output schema covers return values, but the input side is essentially 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% — no parameter has any description. The description names distributions (which merely restates the existing enum) and alludes to link functions, but leaves file_path, dependent, predictors, categorical, scale, and save_predicted entirely unexplained. It does not compensate for the coverage gap.

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 clear verb and resource ('Run generalized linear model (GENLIN)') and characterizes the scope via flexible distribution/link functions. It does not explicitly differentiate from close siblings like spss_glm_univariate, spss_logistic_regression, or spss_genlinmixed, so the agent must infer the boundary.

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?

No when-to-use guidance or alternatives are given. In a family crowded with regression models (logistic, ordinal, glm_univariate, genlinmixed), the absence of routing hints is a real gap.

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