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by HB-0921
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Python
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fanuc_generate_ls_draft

Generate an offline FANUC LS draft from structured input, enabling non-production testing and review without affecting live robot programs.

Instructions

Generate an explicitly non-production FANUC LS offline draft from structured input.

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": {
      "draft": "<draft>"
    },
    "name": "fanuc_generate_ls_draft"
  }
}

Input Schema

NameRequiredDescriptionDefault
draftYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.4/5.0
Behavior3/5

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

With no annotations, the description carries full behavioral burden. It does disclose that the generated output is explicitly non-production and offline, which are useful safety cues. However, it does not mention side effects, output format, whether files are written, or any validation behavior.

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 description is a single tight sentence with no filler. It front-loads the action and purpose, though it is slightly terse given how little parameter documentation exists.

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

Completeness2/5

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

While the output schema exists and may explain return values, the tool has no parameter documentation, no usage context, and no annotations. For a tool accepting a completely open nested object, the description is too minimal to allow an agent to construct a valid invocation confidently.

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 has one undocumented object parameter with 0% description coverage, so the description must compensate. Saying 'from structured input' adds a little meaning, but it does not explain the shape, required fields, or semantics of the 'draft' object. This is insufficient for an opaque nested object parameter.

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 uses a specific verb ('generate') and names both the resource ('FANUC LS offline draft') and the input ('structured input'). The phrase 'explicitly non-production' clarifies intent and differentiates it from parsing/linting siblings.

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?

The description implies this tool is for drafting FANUC LS content before further analysis, parsing, or linting by siblings. However, it does not explicitly state when to prefer this tool over alternatives, nor does it mention any workflow ordering or exclusions.

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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