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record_decision

Document each modeling decision—what was chosen, why, on what evidence, and what was set aside—to keep VARMA analysis choices traceable and reproducible.

Instructions

Record a decision, WHY, on WHAT evidence, and what was set aside.

Every node that opens a decision (which files, which candidate, keep the VARMA or stay with the univariates, what to do with a fit on the MA wall, which Cholesky order) should leave one. Example: node "N5", decision "stay with the univariates", reason "the VARMA gains in no cell at h >= 6", evidence "RMSE ratio 1.02-1.11 over 36 origins", alternatives "VARMA(1,0) full: better in sample (LR p 0.01), worse out of sample". In the AUTONOMOUS lane this is where your reasoning is written down, with decided_by="LLM"; a decision without its reason is not documented.

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>",
      "node": "<node>",
      "decision": "<decision>"
    },
    "name": "record_decision"
  }
}

Input Schema

Name	Required	Default
`name`	Yes
`node`	Yes
`reason`	No
`decision`	Yes
`evidence`	No
`decided_by`	No	analyst
`alternatives`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.0Oct 30, 2026

TDQS

A3.8/5.0

Behavior3/5

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

With no annotations, the description carries the full behavioral burden. It discloses that this is an audit-trail record and that autonomous-lane reasoning uses decided_by="LLM", but it does not clarify append vs overwrite behavior, duplicate handling, permissions, or editability; it also says a decision without its reason is not documented even though reason is optional in the schema.

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 opening sentence is front-loaded and states the core action. The example is lengthy but directly illustrates the key fields, and there is no obvious filler.

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?

The output schema exists, so return values need not be described. However, with no annotations and seven parameters, the description still leaves important things to inference: the meaning of name, mutation behavior, and whether fields like reason are truly required.

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 usefully explains node, decision, reason, evidence, alternatives, and the decided_by="LLM" case, but it never explains the required name parameter or the optional/required status of the other fields.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource: record a decision with its reason, evidence, and alternatives. It distinguishes this audit-trail action from the analysis-oriented sibling tools through examples and the autonomous-lane note.

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 clearly says every node that opens a decision should leave one and gives concrete examples such as choosing files, candidates, VARMA vs univariates, MA-wall fits, and Cholesky order. It does not explicitly state when not to use it or name an alternative tool, but the usage context is strong.

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