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AI-ML-Lab-MCP

AI-ML-Lab-MCP

by SyedHassan904
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Python
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Standardize Features

standardize_features

Scales numerical dataset columns to zero mean and unit variance, enabling fair distance and gradient-based model training such as linear models, SVM, and KNN.

Instructions

Standardize numerical columns to mean 0 and standard deviation 1 (z-score). Good for linear models, SVM, KNN and anything distance or gradient based. Args: dataset_id (str): ID of a dataset or of a split. columns (list[str], optional): Numerical columns to standardize. If omitted, all numerical columns. Returns: dict: Standardized columns with their fitted means and standard deviations.

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": "standardize_features"
  }
}

Input Schema

NameRequiredDescriptionDefault
columnsNo
dataset_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.5/5.0
Behavior3/5

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

With no annotations at all, the description carries the full burden. It usefully discloses that fitted means and standard deviations are returned, and that omitting columns applies the transform to every numerical column, but it does not state whether the dataset is modified in place or a new dataset/result is produced, nor whether the operation is reversible. For an unsupervised mutation on a dataset, that omission is material.

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 purpose statement is front-loaded, and the Args/Returns blocks are compact and scannable. The only mild redundancy is restating parameter names already present in the schema, but the added semantics justify the space.

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

Completeness3/5

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

For a two-parameter transformation with no annotations and no output schema, the description covers inputs and the shape of the return value reasonably well. It leaves out the side-effect model (in-place vs new dataset), the relationship to the very similar scale_features/normalize_features siblings, and error behavior, which keeps it from being complete.

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 description coverage is 0%, so the description must document both parameters itself, and it does: dataset_id is described as an ID of a dataset or of a split, and columns is explicitly optional with a stated default of all numerical columns. That is enough meaning to invoke correctly, with only minor gaps such as what happens if a non-numerical column is passed.

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?

States a specific verb and resource plus the exact transformation semantics (mean 0, sd 1, z-score), which is far more informative than the title alone. It does not distinguish itself from the closely related siblings scale_features and normalize_features, which is the main residual ambiguity.

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?

Gives downstream-use context ('good for linear models, SVM, KNN, distance or gradient based'), which implies when the transformation is appropriate. However it never says when to prefer it over scale_features or normalize_features, nor any prerequisite such as requiring numeric dtype only, so an agent must infer the choice among near-duplicate siblings.

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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