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load_data

Loads raw Excel or CSV time-series tables in original levels, checks completeness on a common calendar, and prepares the series for VARMA modeling in sima.

Instructions

N0r — RAW DATA: an analyst with series and no univariate models (docs/STUDY-raw-entry.md). An Excel (.xlsx/.xls) or CSV table, one column per series in ORIGINAL levels (never transformed or differenced), a header row with the names, and optionally a first column of dates (a year, or year-period: 1972-08, 1972Q3).

freq: 1, 4 or 12 (0: inferred from the date column). start: the first date, "1972-08" or "1850" (empty: from the date column). The table must be complete on one common calendar: missing values are refused, with where. Next: characterize — each series' transformation with art's engine.

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

Input Schema

Name	Required	Description	Default
`freq`	No
`name`	Yes
`path`	Yes
`start`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.0Oct 30, 2026

TDQS

A3.6/5.0

Behavior4/5

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

With no annotations, the description carries the safety burden and does well: it discloses that data must be in original levels (never transformed/differenced), that one common calendar is required, and that missing values are refused with location. It omits error behavior for malformed files or permission concerns, but the core constraints are clear.

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

Conciseness3/5

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

The core content is front-loaded and reasonably compact, but the opening ('N0r — RAW DATA', 'art's engine') is idiosyncratic jargon that costs the reader time, and some phrasing is more cryptic than necessary.

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 values need not be explained, and the description covers the file format, frequency inference, and missing-value policy. Gaps remain around the `name` parameter, error behavior beyond missing values, and any size or locking constraints for the file path.

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%, so the description must compensate and largely does: `freq` is defined as 1/4/12 with 0 meaning inferred from the date column, and `start` is defined with accepted formats and empty meaning derived from the date column. `name` and `path` are only implicitly explained (a study name and an Excel/CSV file), which keeps it from a 5.

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?

The description states a specific verb+resource: loading a raw Excel/CSV table whose columns are series in original levels, with a header row and optional date column. It is concrete about the file shape, though it does not explicitly distinguish itself from siblings like load_example or load_pre.

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?

It implies the entry point ('an analyst with series and no univariate models') and names the next step ('Next: characterize'), which gives workflow context. However, it never states when not to use this tool or which sibling to prefer for other entry paths.

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