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ReliaSim

Get line model walkthrough

get_chapter_narrative
Read-onlyIdempotent

Long-form narrative for the selected chapter — what the chapter adds to the complexity ladder and the key teaching point. Use when the user asks 'walk me through this' or wants the conceptual primer. Pure prose, no numerical claims; safe to summarize.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_chapter_narrative"
  }
}

Input 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?

Annotations already declare readOnly, idempotent, non-destructive, closed-world behavior, so the safety profile is covered. The description adds genuine value beyond that by characterizing the output as pure prose with no numerical claims and noting it is safe to summarize — relevant guidance for how an agent should relay the result.

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?

Three short sentences, front-loaded with the resource and output shape, then the usage trigger, then a caveat about numerical content. No filler and nothing repeated from structured fields.

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?

With one fully documented enum parameter, no output schema, and rich annotations, the description covers what the tool returns (prose narrative, conceptual primer) and when to call it. An agent has enough to invoke it correctly; only the routing against get_chapter_facts and compare_chapters remains implicit.

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 100% and the single enum parameter is documented in detail, including both track semantics and the default. The description only says "the selected chapter" and adds no meaning beyond the schema, so the baseline of 3 applies.

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?

States a specific verb+resource ("Long-form narrative for the selected chapter") and scopes the content to the complexity ladder and key teaching point. It implicitly distinguishes itself from get_chapter_facts by declaring "Pure prose, no numerical claims," 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?

Gives a concrete trigger ("Use when the user asks 'walk me through this' or wants the conceptual primer"), which is actionable and maps to a real user intent. It lacks an explicit when-not clause or a named alternative for numerical questions, leaving that routing to inference.

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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