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CongJyu

spss-studio-mcp

by CongJyu

Spss Read Data

spss_read_data

Read rows from an SPSS .sav file into a Markdown table without SPSS installed. Filter variables and limit rows to inspect or share data quickly.

Instructions

Read rows of data from an SPSS .sav file as a Markdown table. Optionally filter to specific variables and limit row count. Does not require SPSS to be installed.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
max_rowsNo
file_pathYes
variablesNo
apply_value_labelsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.2/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It usefully discloses the return format (Markdown table) and the notable constraint that SPSS need not be installed, which is real behavioral context. It does not say what happens on missing files, whether output is truncated when max_rows is hit, or any permission/environment caveats.

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, front-loaded with the core action and output format, then options, then the environment caveat. Nothing is padded, though the middle sentence is somewhat thin relative to the gaps left elsewhere.

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

Completeness3/5

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

An output schema exists, so return-value detail is not required. However, for a 4-parameter tool with 0% schema coverage, the description leaves key behaviors (truncation signaling, value-label semantics) undocumented, so it is only minimally sufficient.

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 4 parameters, so the description must compensate and largely does not. It explains the intent behind 'variables' and row limiting, but says nothing about max_rows' default of 50, its truncation semantics, or the meaning and effect of apply_value_labels (raw codes vs. labeled values).

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 ('Read rows of data from an SPSS .sav file') plus the output format ('as a Markdown table'), which is more informative than the title restatement. It implicitly separates itself from metadata/summary siblings, but never names them, so an 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 Guidelines3/5

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

The mention of optional variable filtering and row limiting implies when the tool is useful for targeted inspection, but there is no explicit when-to-use / when-not-to-use guidance and no reference to alternatives like spss_read_metadata or spss_file_summary that also read from a .sav file.

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