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Show buffer size tradeoff

run_buffer_tradeoff
Read-onlyIdempotent

Buffer Tradeoff experiment — sweep a buffer's capacity from 50 → 10,000 units, measure throughput gain. Shows the diminishing-returns elbow for buffer sizing. Only defined on bs4-ct and bs4-leds; each chapter has THREE inline buffers with different placements (pass buffer id to pick one). Compare CT vs LEDS on the same slot to see why interrupt-detail level changes buffer ROI math (e.g. b3: CT +23.7% vs LEDS +64.2%). Use when the user asks 'how big should the buffer be?' / 'do buffers help on this line?' / 'which buffer position gives the most gain?' / 'what's the diminishing-returns point?'. ANTI-FABRICATION (CRITICAL): the specific tradeoff numbers (e.g. CT +23.7% vs LEDS +64.2%) are sweep-derived reference values. Quote VERBATIM in your reply; do NOT recall similar percentages from training data — every buffer position has different math.

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": "run_buffer_tradeoff"
  }
}

Input Schema

NameRequiredDescriptionDefault
bufferNoBuffer id to sweep. The Buffer-Options Constraint-Level model has `b3` (Buffer 1, between Capper↔Labeler), `b4` (Buffer 2, between Labeler↔Case Packer), `b5` (Buffer 3, between Case Packer↔Palletizer). The Buffer-Options LEDS model has `b2` (Buffer Option 1, earliest), `b3` (Buffer Option 2, middle), `b4` (Buffer Option 3, last). Defaults to b3 if omitted — but pick the buffer that matches the question (e.g. 'the first inline buffer' = b3 on CT, b2 on LEDS).b3
chapterNoChapter id. Only `bs4-ct` and `bs4-leds` have buffer tradeoffs defined.bs4-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

A4.6/5.0
Behavior5/5

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world), and the description adds real context on top: what the sweep does, the elbow it surfaces, the chapter restriction, and that returned percentages are sweep-derived reference values to quote verbatim rather than reconstruct. That anti-fabrication instruction is exactly the kind of behavioral guidance annotations cannot express.

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?

Front-loaded with what the tool does and the chapter constraint before the usage triggers, so an agent gets the essentials first. The closing ANTI-FABRICATION block is emphatic and slightly long, but it carries non-redundant instructions, so the length is largely justified.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a two-parameter read-only experiment tool with no output schema, the description covers what is measured, which chapters support it, how to pick the buffer, and how to treat the returned numbers. An agent has everything needed to invoke it correctly and interpret the result.

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

Parameters4/5

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

Schema coverage is 100% and the buffer parameter is already well documented, yet the description adds cross-model mapping that the schema does not fully spell out — 'the first inline buffer' is b3 on CT but b2 on LEDS, and each chapter has three buffers with different placements. That disambiguation genuinely reduces mis-selection beyond the schema text.

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?

Opens with a specific verb and resource — sweep a buffer's capacity from 50 → 10,000 units and measure throughput gain — and names the analytical output (diminishing-returns elbow). It also scopes the tool to two chapters, which separates it from general tools like run_gain_loss or compare_chapters.

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 four explicit user-question triggers ('how big should the buffer be?', 'do buffers help on this line?', etc.) plus the hard precondition that only bs4-ct and bs4-leds define tradeoffs. It does not name alternative sibling tools for out-of-scope chapter questions, so routing when the chapter is unsupported is left 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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