RQM WaveEngine Jobs

Fit Coupled Channel Model

wave_fit_coupled_channel_model_v1

Idempotent

Problem: Fit a bounded structured quaternion ridge model to these coupled channel features and targets. Input: JSON with feature count, output count, features, targets. Result: fitted channel model and fit evidence. Limits: Bounded signal processing only; 16 channels; 512 samples/channel.

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": {
      "schema_version": "<schema_version>",
      "request": "<request>",
      "idempotency_key": "<idempotency_key>",
      "max_total_price": "<max_total_price>"
    },
    "name": "wave_fit_coupled_channel_model_v1"
  }
}

Input Schema

Name	Required	Description	Default
`request`	Yes
`schema_version`	Yes
`idempotency_key`	Yes
`max_total_price`	Yes

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedOct 25, 2026

changedOutput schema / anyOf

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+          "FEATURE_DISABLED",
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+          "QUOTE_NOT_AVAILABLE",
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First observedSep 12, 2026

TDQS

A3.7/5.0

Behavior4/5

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

Annotations already declare idempotent=true, destructive=false, readOnly=false, and closed-world. The description adds useful behavioral context beyond those: the result includes fit evidence, and the input is constrained to bounded signal processing with 16 channels and 512 samples per channel. It does not explain pricing or job submission semantics, but it is consistent with the annotation profile.

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?

The Problem/Input/Result/Limits structure is compact and front-loaded, and every line contributes either purpose, input shape, output expectation, or a hard limit.

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 description covers the core modeling request and output, and an output schema exists for return values. However, for a tool with a nested request envelope and a fixed max_total_price, it omits important context about idempotency, cost, and the expected shape of features/targets, so an agent could still misconstruct the call.

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 needs to carry parameter meaning. It explains the likely payload fields (feature count, output count, features, targets) but says nothing about schema_version, idempotency_key, or max_total_price, leaving key envelope parameters undocumented.

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 gives a specific verb and model type: fit a bounded structured quaternion ridge model to coupled channel features and targets. That distinguishes it from siblings like wave_extract_wave_features or wave_optimize_processing_pipeline, though it never names an alternative explicitly.

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?

Usage is implied by the problem statement and the limits (bounded signal processing, 16 channels, 512 samples/channel), but there is no explicit when-to-use versus siblings guidance or when-not-to-use condition.

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

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