hidim

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load_dataset

Load files into memory as tables or tensors and return a schema summary so you can inspect dataset structure without transferring the data.

Instructions

Load a file into memory as a table (rows x columns) or tensor (N-d labelled array). Returns a schema summary, never the data.

Formats by extension (or format): csv tsv txt parquet feather json jsonl xlsx npy npz mat h5/hdf5 nc zarr. options: pandas/reader keyword arguments, e.g. {"sep": ";", "index_col": 0, "parse_dates": ["date"]}, {"sheet": "Sheet2"}, {"key": "array_name"} for npz/mat/h5/nc with several arrays. Relative paths resolve against the first HIDIM_ROOTS entry (home by default). npz files written by save_dataset come back as tensors with their dims and coordinates intact.

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": {
      "path": "<path>"
    },
    "name": "load_dataset"
  }
}

Input Schema

Name	Required	Description	Default
`name`	No
`path`	Yes
`format`	No
`options`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.1Nov 9, 2026

TDQS

A4.1/5.0

Behavior4/5

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

With no annotations, the description carries the full burden and does a solid job: it discloses that only a schema summary is returned (not data), how relative paths resolve (first HIDIM_ROOTS entry, home by default), and that npz files written by save_dataset round-trip with dims and coordinates intact. It stops short of failure modes, permission/auth requirements, or size limits.

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?

The critical facts (what is loaded, what is returned) are front-loaded, and each following block covers formats, options, path resolution, and round-trip behavior without filler. The long extension list and inline examples add bulk but each earns its place as reference material.

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?

For a tool with an output schema, the description still usefully flags that the return is a schema summary, and it covers format handling, option passing, and path resolution. The notable gap is the unexplained `name` parameter and no mention of error behavior for unsupported or missing files.

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

Parameters4/5

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

Schema coverage is 0% across four parameters, so the description must compensate, and it largely does: `format` is tied to extension inference, `options` is illustrated with concrete pandas, sheet, and multi-array `key` examples, and `path` resolution is explained. Only `name` is left undefined, leaving one of four parameters undocumented.

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?

States a specific verb and resource ('Load a file into memory as a table ... or tensor') and immediately clarifies the key ambiguity about what comes back ('Returns a schema summary, never the data'). An agent can distinguish this from save_dataset, list_datasets, or describe without opening any schema.

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 rather than stated: the description tells you how to specify formats and options but never says when to choose load_dataset over siblings like list_datasets or describe, nor any precondition for calling it. The path-resolution note is helpful context but not selection guidance.

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