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run_gate

Checks whether a joint VARMA model reproduces its univariate fits via the exact log-likelihood identity, stops analysis if the diagonal gate fails, and flags each file as an optimum or specification.

Instructions

N1 — The diagonal gate: does the joint cast reproduce the univariate models?

Each series is fitted alone on the common window; the diagonal system is then evaluated at those optima. The identity logL_joint = SUM logL_i is exact. If it fails, the analysis STOPS: nothing estimated on top of a joint model that does not reproduce its parts can be trusted.

It also says whether each file is an OPTIMUM (it does not move when re-estimated) or a SPECIFICATION (it moves), which decides whether the yardstick is the analyst's certified model or not.

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": "run_gate"
  }
}

Input Schema

Name	Required	Description	Default
`name`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.0Oct 30, 2026

TDQS

B3.3/5.0

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 check is exact, that failure halts the analysis, and that the returned classification (OPTIMUM vs SPECIFICATION) changes how results should be interpreted. It stops short of stating cost, side effects, or what 'name' selects, but the behavioral profile is unusually well disclosed for an unannotated tool.

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 identity and stop rule are front-loaded and earn their place, but the 'N1 —' prefix, the interrogative opening, and the OPTIMUM/SPECIFICATION digression add length without clarifying invocation. Capitalized emphasis is used efficiently on the STOP consequence, though the middle paragraph is denser than needed.

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-value detail is fairly excused, and the conceptual semantics of the gate are well covered. The gap is operational: with a required 'name' parameter and 0% schema coverage, an agent still cannot tell what to pass or what the check will fit, which is a material omission for a one-parameter tool.

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

Parameters2/5

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

Schema coverage is 0% and the description never explains the single required parameter 'name' — it is unclear whether it names a series, a file, or a model run. The phrase 'whether each file is an OPTIMUM or a SPECIFICATION' hints that files are involved but does not connect to the parameter.

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 — a diagonal gate that re-fits each series alone and verifies that the joint likelihood equals the sum of the univariate likelihoods — which is distinct from estimation siblings like estimate or estimate_vec. The 'N1' shorthand and the interrogative framing ('does the joint cast reproduce...?') make the first line slightly obscure, but the identity logL_joint = SUM logL_i pins the purpose precisely.

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 gives a hard stop rule ('If it fails, the analysis STOPS: nothing estimated on top of a joint model that does not reproduce its parts can be trusted'), which implies this must run before downstream estimation. However, no sibling tool is named as a prerequisite or alternative (e.g., build_univariate), so the agent must infer placement in the workflow.

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