OriginLab-MCP

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origin_plot_option_get

Retrieves and returns the current value of a specific LabTalk property (like line width or fill pattern) from a plot in an Origin graph, using its option token.

Instructions

Read one LabTalk set option off a data plot.

option is a LabTalk option token such as -w (line width, in points x 500) or -pfp (bar fill pattern). Use origin_set_options to find one -- the names are not guessable, and an invented one would be accepted by Origin and do nothing.

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": {
      "graph": "<graph>",
      "option": "<option>"
    },
    "name": "origin_plot_option_get"
  }
}

Input Schema

Name	Required	Description	Default
`plot`	No
`graph`	Yes
`layer`	No
`option`	Yes

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 23, 2026

TDQS

A4.4/5.0

Behavior4/5

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

No annotations are provided, so the description must carry the full behavioral burden. It clearly states the operation is a read ('Read') and gives concrete examples of options and their semantics. It also warns about the risk of using invalid options. However, it doesn't describe potential side effects (none expected), error cases, or return format, but the output schema covers the return. Overall adequate.

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?

Two sentences, front-loaded with the core purpose, then a crucial usage hint about option names and the companion tool. No fluff, highly efficient.

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 no annotations, the description carries the full behavioral burden. It discloses that 'option' is a LabTalk token, gives examples, warns about invalid names, and directs to origin_set_options. It doesn't explain return format, but an output schema exists. The parameter semantics for graph/plot/layer are left implicit, though likely inferable from the context of Origin data plots. Overall quite complete but not exhaustive.

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 has no descriptions (coverage 0%), so the description must compensate. It explains the 'option' parameter well, giving examples and warning about guessabilityemanding reference to origin_set_options. However, it does not explain the semantics of 'graph', 'plot', and 'layer' parameters, leaving the agent to infer they refer to plot identification. Some compensation, but incomplete.

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 states the tool reads a LabTalk set option from a data plot, with a specific verb ('Read') and resource ('data plot'). It differentiates from sibling origin_plot_option_set by focusing on read versus set, and names the companion tool origin_set_options for discovering valid options. This makes the purpose unambiguous and distinct.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly instructs to use origin_set_options to find option namesaine a non-obvious set, and warns that invented options are accepted but ineffective. This provides clear when-to-use and when-not-to-use guidance, effectively distinguishing this read tool from its set counterpart.

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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