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CongJyu

spss-studio-mcp

by CongJyu

Spss File Summary

spss_file_summary

Summarize an SPSS .sav file locally without SPSS: get case and variable counts, variable list, and basic descriptive statistics. Requires a file path.

Instructions

Get a summary of an SPSS .sav file: case count, variable count, variable list, and basic descriptive statistics computed locally (no SPSS needed). Does not require SPSS to be installed.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
file_pathYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.5/5.0
Behavior3/5

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

With no annotations, the description carries the full burden, and it does disclose one meaningful behavioral trait: the summary is computed locally and does not require an SPSS installation. That is genuinely useful for a suite of SPSS tools that presumably need a backend. It says nothing about error behavior for a missing or malformed file, performance on large .sav files, or any permission requirements.

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?

Front-loaded with the operation and its outputs in one dense sentence. The trailing sentence restates 'no SPSS needed' from the parenthetical, which is mild redundancy, but overall it is short and earns most of its space.

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

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists, so enumerating return contents goes beyond necessity, and nothing critical is missing for a single-parameter read tool. Gaps like behavior on unreadable files or large-file performance are minor for this complexity level.

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

Parameters3/5

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

Schema description coverage is 0% for the single file_path parameter, so the description must compensate. It partially does by implying the argument is a path to an SPSS .sav file, which constrains the expected format, but it adds no guidance on absolute vs relative paths or how the file is located.

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 ('Get a summary of an SPSS .sav file') and enumerates exactly what is returned: case count, variable count, variable list, and basic descriptive statistics. It is clear enough to distinguish from deep-analysis siblings, though it does not explicitly contrast with overlapping tools like spss_read_metadata or spss_descriptives.

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?

Usage is implied by the content list (a fast file-level overview usable before deeper analysis), and the 'computed locally' note hints at when it is appropriate. However, no alternative sibling is named and no condition for choosing this over spss_read_metadata or spss_descriptives is stated.

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