spss-studio-mcp

Overview Schema Source Related Servers Discussions Admin

Spss Twostep Cluster

spss_twostep_cluster

Run two-step cluster analysis with automatic cluster number determination. Handles large datasets and mixed variable types.

Instructions

Run two-step cluster analysis with automatic cluster number determination. Handles large datasets and mixed variable types. 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>"
    },
    "name": "spss_twostep_cluster"
  }
}

Input Schema

Name	Required	Default
`distance`	No	LIKELIHOOD
`file_path`	Yes
`continuous`	No
`categorical`	No
`max_clusters`	No
`num_clusters`	No
`outlier_handling`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 12, 2026

TDQS

B3.1/5.0

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 does disclose one important operational requirement ('Requires IBM SPSS Statistics to be installed'), but is silent on runtime cost, determinism, how outlier_handling or num_clusters vs max_clusters interact, and what errors to expect. Useful prerequisite context, but significant behavioral gaps remain.

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 no filler; the core purpose and its differentiators are front-loaded, with the environment prerequisite placed last. Efficient, though the terseness leaves room that the parameter gaps should have used.

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 described, but for a 7-parameter analysis tool with zero annotation and zero schema-description coverage the entry is too thin. Key calling decisions (distance metric meaning, num_clusters vs max_clusters precedence, variable-list construction) 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 7 parameters, yet the description mentions none of them. It does not explain distance choices, what the continuous/categorical lists expect, or the relationship between num_clusters (fixed k) and max_clusters (automatic search ceiling) — precisely the ambiguity an agent would need resolved.

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 and resource ('Run two-step cluster analysis') plus the distinctive capability ('automatic cluster number determination. Handles large datasets and mixed variable types'), which implicitly separates it from spss_cluster_hierarchical. It never names the sibling explicitly, so an agent must infer the routing from characteristics rather than a direct pointer.

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

Usage Guidelines3/5

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

The clause 'Handles large datasets and mixed variable types' implies the conditions under which this tool is preferred over hierarchical clustering, but there is no explicit when-to-use/when-not guidance and no mention of alternatives by name. Usage is inferable but not stated.

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

Deploy Server