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Ian3738
by Ian3738

data_load

Load a data file into an R session for use by other statistical tools. Supports CSV, Excel, SPSS, Stata, SAS, RDS, RData, JSON, and Parquet, returning dimensions and column summaries.

Instructions

Load a data file into the session under a name. Supports CSV, TSV, Excel (.xlsx/.xls), SPSS (.sav), Stata (.dta), SAS (.sas7bdat), RDS, RData, JSON and Parquet; the format is detected from the extension. Returns the dimensions and a per-column summary of types, missing values and example values. The dataset is then available to every other tool as data="<name>".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sepNoField separator for delimited text, e.g. ';' or '\t'.
nameNoName to give the dataset in the R session.df
pathYesPath to the file. `~` is expanded.
skipNoRows to skip before the header.
sheetNoExcel sheet name or number.
formatNoOverride the detected format: csv, tsv, delim, excel, spss, stata, sas, rds, rdata, json, parquet.
headerNoWhether the first row holds column names.
encodingNoFile encoding, e.g. UTF-8, latin1, big5.UTF-8
max_rowsNoRead at most this many rows.
na_stringsNoStrings to treat as missing.
labels_to_factorNoFor SPSS/Stata, convert labelled numeric columns into factors.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations, the description carries the full behavioral burden. It discloses the side effect (dataset available session-wide), the return value (dimensions and per-column summary), and format detection from extension. It does not mention overwrite behavior if a name is reused, but the disclosed behavior is substantial.

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

Conciseness5/5

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

Three sentences, front-loaded with the main action, followed by formats, return behavior, and reuse. Every sentence earns its place and there is no filler.

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

Completeness5/5

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

For a load tool with no output schema, the description explains what the call returns, how formats are inferred, and how to reference the loaded dataset later. The optional parameters are well covered by the schema, so nothing essential is missing.

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 100%, so the baseline is 3. The description adds useful high-level context about format detection and naming, but does not go beyond what the parameter descriptions already provide.

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

Purpose5/5

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

The description clearly states a specific verb and resource: 'Load a data file into the session under a name.' It enumerates supported formats and the session-level effect, making it easy to distinguish from sibling tools like data_list, data_inspect, or data_export.

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

Usage Guidelines4/5

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

The description gives clear context for when to use the tool: it loads external file formats into the session and makes them available to all other tools as data="<name>". It does not explicitly name alternatives or exclusions, so it stops short of a 5.

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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