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load_pre

Starts a simultaneous VARMA session by loading univariate .pre or .inp files in Cholesky order; aligns series by frequency and last date and rejects mismatched end dates.

Instructions

N0 — Start a session from the univariate models: one fue file per series.

paths_json is a JSON list of paths to fue files — .pre (optima, from art or fue) or .inp (specifications) — in the order you want the system (it is also the Cholesky order of the impulse responses). This is how you arrive from mtram when it found a cycle: load the SAME .pre files it had.

The files are read by fue's own parser and recognised by their content. The series must share their frequency and their LAST date (BUG-2): different start dates are aligned at the end; different ends are refused.

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>",
      "paths_json": "<paths_json>"
    },
    "name": "load_pre"
  }
}

Input Schema

Name	Required	Description	Default
`name`	Yes
`paths_json`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.0Oct 30, 2026

TDQS

A4/5.0

Behavior4/5

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

With no annotations, the description carries the load here and does well: it discloses that files are content-recognised by fue's own parser, that `.pre` may come from art or fue, and the hard constraint (BUG-2) that series must match frequency and last date, with end-aligned starts and refusal on differing ends. It could go further on what state the session is left in on failure, but the constraint and error behaviour are unusually well covered.

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?

Front-loads the core action in the first sentence, then adds only details that change behaviour (ordering semantics, parser recognition, BUG-2 alignment rule). No filler; every clause carries information an agent needs to call it correctly.

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 return values need no explanation, and the description covers constraints, file formats, and ordering adequately. The one real gap is the unexplained `name` argument, which matters for a tool that mutates session state.

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 coverage is 0%, so the description must compensate. It does so thoroughly for `paths_json` — JSON list of paths, accepted extensions, and that ordering drives the system/Cholesky order of impulse responses. However, the required `name` parameter is never explained, leaving one of two parameters undocumented anywhere.

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: start a session from univariate models, one fue file per series, with `.pre`/`.inp` files read by fue's parser. It distinguishes this entry point from the broader load sibling set by tying it to univariate models and the mtram-cycle arrival path, though it never names `load_data` or `load_example` explicitly.

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?

Gives clear context for when to use it: arriving from mtram after a cycle, loading the SAME `.pre` files it had, or starting from univariate models. It does not explicitly exclude or contrast with the other load_* alternatives in the sibling list, so it stops short of full when-not guidance.

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