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run_gain_loss

Gain/Loss experiment — disable each interrupt one at a time, measure production recovered. Reveals the ACTUAL impact of each failure mode (Gain ≠ Loss: removing one lets others fire more often). Available on bs1-leds, bs3-leds, bs4-ct, bs4-leds. Use when the user asks 'what if we fixed X?' / 'which interrupt matters most if we actually fixed it?' / 'show me the Pareto'. ANTI-FABRICATION: per-interrupt recovered-production numbers come from real dys-cli runs. Quote VERBATIM; the Gain ≠ Loss interaction is exactly the kind of figure LLMs are prone to fabricate — don't.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
chapterNoWhich curriculum chapter the tool should answer about. Format: `bs<1-5>-<ct|leds>`. Both tracks run on the same real plant data — `ct` = Constraint-Level (interrupts rolled up to one Weibull per machine, 5 total) and `leds` = LEDS-Level (interrupts drilled down to named failure modes, 36 total). Defaults to bs1-ct when omitted.bs1-ct

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / chapter / enum
      Previous value: -[
-  "bs1-ct",
-  "bs2-ct",
-  "bs3-ct",
-  "bs4-ct",
-  "bs1-leds",
-  "bs2-leds",
-  "bs3-leds",
-  "bs4-leds"
-]New value: +[
+  "bs1-ct",
+  "bs2-ct",
+  "bs3-ct",
+  "bs4-ct",
+  "bs1-leds",
+  "bs2-leds",
+  "bs3-leds",
+  "bs4-leds",
+  "cmp-buffer-reliability",
+  "cmp-shared-palletizer"
+]
  2. First observed

TDQS

A4/5.0
Behavior4/5

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

With no annotations, the description carries the behavioral disclosure burden. It usefully reveals the Gain ≠ Loss interaction, states that numbers come from real dys-cli runs, and warns against fabricating figures. It doesn't discuss side effects or output shape, but the provenance warning is valuable behavioral context.

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 description is compact and well structured: definition, key interaction, availability, usage triggers, and a critical anti-fabrication warning. Every sentence earns its place and the most important information is front-loaded.

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?

For a single-parameter tool with no output schema, the description covers purpose, use cases, and data provenance reasonably well. The conflicting availability list, especially excluding the schema default, leaves an unresolved ambiguity that prevents the description from being fully dependable for invocation.

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?

The schema already documents the `chapter` parameter fully, so the baseline is 3. However, the description's 'Available on' list contradicts the schema: it omits the default `bs1-ct` while the enum includes many values not listed. This conflicting guidance hurts rather than helps parameter interpretation.

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 clearly identifies the tool as a Gain/Loss experiment that disables interrupts one at a time and measures recovered production. It also highlights the Gain ≠ Loss interaction, which distinguishes it from ordinary bottleneck or explanation tools.

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?

The description gives concrete 'Use when' triggers with user phrasing and a list of applicable chapters. It does not name an alternative sibling tool or explicitly say when not to use it, but the usage context is clear enough for an agent to select it.

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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