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study_estimation

Diagnose suspect VARMA fits by checking inverse roots, comparing an independent exact likelihood, and re-optimizing from retreat points to test whether the reported optimum is a true maximum.

Instructions

N4b — Study the current fit when the estimation may be ill-defined.

For a fit that stopped on the MA invertibility wall (estimate says so), or any fit whose optimum you doubt. Evidence, then a menu; no verdict:

  1. the AR and MA inverse roots of the joint model, and the pairs that nearly cancel (a near-common factor makes the likelihood a ridge);

  2. a SECOND PATH: the same model optimised with Shea's exact likelihood (AS 242) instead of elf (AS 311) -- same function, independent code, another path;

  3. RESTARTS: from the stopping point, step back towards the start (x = start + retreat * (stop - start)) and re-optimise; repeat while the likelihood rises. If it keeps rising, the ridge climbs along the wall and the reported point is not a maximum.

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": "study_estimation"
  }
}

Input Schema

NameRequiredDescriptionDefault
nameYes
retreatNo
restartsNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A3.9/5.0
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 well: it discloses that the tool returns evidence and a menu with 'no verdict', details the three diagnostic paths, and explains the restart behavior (stepping back to start and re-optimising while likelihood rises). It does not clarify whether re-optimisation mutates the stored fit state, which is the main remaining gap for a stateful analysis tool.

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 purpose and triggering condition are front-loaded, and the numbered evidence list is scannable. It is dense with domain jargon but every clause carries diagnostic meaning; slightly long but not padded.

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 when to call, what it does, and how restarts behave. The remaining gap is the meaning of the required 'name' argument and whether the fit is modified in place.

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 0%, so the description must compensate. It does explain 'retreat' well through the formula x = start + retreat * (stop - start), and implies 'restarts' via 'repeat while the likelihood rises', but it never explains what the required 'name' parameter selects. Partial compensation only.

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 verb (study/diagnose) and a specific resource (the current estimated fit), and clarifies scope with 'when the estimation may be ill-defined.' It distinguishes itself from the plain 'estimate' sibling by being an evidence-gathering diagnostic rather than an estimator, though it never names that sibling 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?

It gives explicit triggering conditions: a fit that stopped on the MA invertibility wall, or any fit whose optimum you doubt. That is clear when-to-use guidance. It stops short of naming alternatives (e.g., estimate, check_residuals) or when-not-to-use.

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