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Origin Pro MCP Server

by youngminsw
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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compare_means

Perform a two-sample t-test comparing means of two specified columns. Returns t-statistic, degrees of freedom, p-value, and group means.

Instructions

Two-sample t-test between two columns.

Returns: JSON: t, df, p_value, mean1, mean2, equal_variance

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": {
      "data_book": "<data_book>",
      "data_sheet": "<data_sheet>",
      "col1": "<col1>",
      "col2": "<col2>"
    },
    "name": "compare_means"
  }
}

Input Schema

NameRequiredDescriptionDefault
data_bookYes
data_sheetYes
col1Yes
col2Yes
equal_varianceNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.2/5.0
Behavior3/5

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

No annotations are provided, so the description must carry the full burden. It discloses the return fields (t, df, p_value, etc.) and mentions the 'equal_variance' parameter, hinting at Welch's t-test by default. However, it omits assumptions, side effects, or permission requirements.

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 very concise and front-loaded with the core action. However, it is too terse, especially missing parameter explanations. It could be slightly more informative without losing conciseness.

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?

For a tool with 5 parameters and a statistical test, the description is incomplete. It lacks prerequisite information (e.g., numeric columns, data existence) and does not guide parameter usage. The output schema is described, but input context is scant.

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

Parameters1/5

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

Schema description coverage is 0% and the description does not explain any parameters (data_book, data_sheet, col1, col2, equal_variance). It only describes the output, leaving parameter semantics entirely to the schema, which lacks descriptions.

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 states 'Two-sample t-test between two columns,' which is a specific verb and resource. It clearly distinguishes from sibling tools like 'column_statistics' or 'curve_fit' that have different statistical purposes.

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 usage for comparing means but does not explicitly guide when to use this tool versus alternatives like 'column_statistics' or 'curve_fit'. No exclusion criteria or prerequisites are mentioned.

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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