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AI-ML-Lab-MCP

AI-ML-Lab-MCP

by SyedHassan904
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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Scale Features

scale_features

Scales numerical dataset columns for ML with standard, min-max, robust, or max-abs methods. Fit the scaler on a training split and specify columns to avoid scaling targets.

Instructions

Scale numerical columns. Recommended: use a split_id so the scaler is fit on training data only. If a dataset id is used, pass columns explicitly so the target column is not scaled. Args: dataset_id (str): ID of a dataset or of a split. columns (list[str], optional): Numerical columns to scale. If omitted, all numerical columns. method (str): "standard" (mean 0, std 1), "minmax" (0 to 1), "robust" (median/IQR, good with outliers) or "maxabs" (divide by max absolute value). Returns: dict: Scaled columns and the fitted scaler parameters.

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": {
      "dataset_id": "<dataset_id>"
    },
    "name": "scale_features"
  }
}

Input Schema

NameRequiredDescriptionDefault
methodNostandard
columnsNo
dataset_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.1/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral burden. It usefully discloses that the scaler can be fit on training data via split_id and describes the return payload as scaled columns plus fitted scaler parameters. However, it does not state whether the dataset is mutated in place, whether a new dataset is created, or how non-numeric or missing values are handled.

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?

The description is front-loaded with purpose and a key recommendation, then structured into Args and Returns sections. Every sentence adds useful information, with no filler.

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?

For a three-parameter tool with no annotations and no output schema, the description covers purpose, all parameters, method semantics, leakage guidance, and the return shape. The main missing piece is whether scaling modifies an existing dataset or returns a separate result, which matters for correct downstream use.

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

Parameters5/5

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

Schema description coverage is 0%, so the description must document the parameters, and it does: dataset_id is a dataset or split ID, columns defaults to all numerical columns when omitted, and method lists four concrete options with their mathematical meanings. This adds substantial meaning beyond the bare schema types.

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?

The description states a specific verb and resource: 'Scale numerical columns.' It clearly covers standard, minmax, robust, and maxabs scaling. However, it does not explicitly distinguish this general scaler from siblings such as standardize_features or normalize_features, which likely overlap in purpose.

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?

It gives clear operational guidance: prefer a split_id so the scaler is fit only on training data, and pass columns explicitly when using a dataset_id so the target is not scaled. These are meaningful when-to-use and avoidance instructions. It does not name alternatives like standardize_features or normalize_features, so it stops short of full routing guidance.

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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