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AI-ML-Lab-MCP

AI-ML-Lab-MCP

by SyedHassan904
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Python
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One Hot Encode

one_hot_encode

Encode categorical dataset columns as separate 0/1 feature columns, with optional first-category dropping to reduce redundancy for linear models.

Instructions

One-hot encode categorical columns: every category becomes its own 0/1 column. Best for nominal columns with few categories (e.g. fuel). Categories that only appear in the test data are encoded as all zeros. Args: dataset_id (str): ID of a dataset or of a split. columns (list[str], optional): Categorical columns to encode. If omitted, all categorical columns. drop_first (bool): Drop the first category of each column to avoid redundancy (useful for linear models). Default is False. Returns: dict: Encoded columns and the new column names.

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": "one_hot_encode"
  }
}

Input Schema

NameRequiredDescriptionDefault
columnsNo
dataset_idYes
drop_firstNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.8/5.0
Behavior3/5

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

With no annotations, the description carries the full burden and does disclose an important trait: unseen test categories become all zeros, which matters for leakage. However, it never states whether the source dataset is mutated in place or a new dataset is produced, nor what errors occur for an invalid dataset_id — the Returns line hints at returned columns but leaves the mutation semantics ambiguous.

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 the core behavior, then a usage hint, then structured Args/Returns sections. Every line carries information; the only minor redundancy is restating defaults that also appear in the schema.

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 transformation with no output schema, the description covers inputs, defaults, the test-set edge case, and the return shape. The remaining gap is mutation/in-place behavior on the source dataset, which matters because there are no annotations to signal side effects.

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 0%, so the description must document the parameters and it does: dataset_id accepts 'a dataset or of a split', columns defaults to all categorical columns when omitted, and drop_first defaults to False with its purpose explained. This adds real meaning beyond the bare schema names, though it does not cover value formats or failure modes for dataset_id.

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 gives a precise verb and resource ('one-hot encode categorical columns') plus the mechanism ('every category becomes its own 0/1 column'), which distinguishes it functionally from label_encode-style siblings even though no sibling is named. It is specific about outcome rather than restating the title.

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?

'Best for nominal columns with few categories (e.g. fuel)' gives clear applicability criteria, and drop_first is justified with 'useful for linear models'. It never names an alternative tool to use for ordinal or high-cardinality columns, so the when-not guidance is implied rather than explicit.

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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