Skip to main content
Glama

simplify

Tests whether weak cross-series coefficients (|t| below a threshold) in the current VARMA fit can be set to zero, refits the restricted model, and compares it with the full fit via LR, AIC, and BIC.

Instructions

N4s — Tiao and Box's (1981, §4) simplification by coefficient, on the current fit: the cross coefficients (and, on route V or in the echelon form, each series' own AR/MA coefficients) with |t| < t are held at zero, the model is refitted, and the restriction is tested by LR against the full fit, with AIC and BIC. The restricted fit is cached: adopting it (the estimate call in the menu) is instant. Route U's univariate models are never touched — they are art's; the echelon form's own part, which replaces their regular ARMA, is sima's, and is simplified like the rest. Several rounds are normal (Tiao and Box simplified the SCC model twice).

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>"
    },
    "name": "simplify"
  }
}

Input Schema

NameRequiredDescriptionDefault
tNo
nameYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A3.5/5.0
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 delivers: it discloses that the restricted fit is cached, that adopting it via the estimate call is instant, and precisely which sub-models are modified versus left alone. It does not state permission/auth or error behavior, but for a statistical refit these are the most valuable behavioral traits.

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?

It is front-loaded with the core action, which is good, but it is overlong and freighted with parenthetical jargon ('art's', 'sima's', the Tiao-Box twice anecdote) that dilutes rather than earns its place. The useful behavioral facts (caching, what's untouched) are buried mid-paragraph.

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 needn't be explained. But with 0% schema coverage and no annotations, the undefined `name` parameter and unexplained route/echelon jargon leave gaps an agent needs filled to invoke it confidently.

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 gives clear meaning to `t` ('coefficients with |t| < t are held at zero', default 1) but leaves `name` completely undefined — presumably the fit identifier, but an agent must guess.

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 names a specific operation on a specific object: Tiao and Box coefficient simplification on the current fit, with coefficients below a t-threshold zeroed, refit, and LR-tested. The core verb+resource is clear, but internal jargon (N4s, route V/U, art's, sima's) clouds what the tool produces for a reader without domain training.

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 iterative use ('on the current fit', 'several rounds are normal') and clarifies scope (route U univariate models untouched). However, it never explicitly contrasts with siblings like test_restriction or estimate, so an agent must infer when to prefer this over those tools.

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