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AI-ML-Lab-MCP

AI-ML-Lab-MCP

by SyedHassan904
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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Find High Correlations

find_high_correlations

Identify numerical feature pairs whose absolute correlation exceeds a chosen threshold to reveal redundant features before modeling.

Instructions

Find highly correlated pairs of numerical features. Args: dataset_id (str): ID of the dataset. threshold (float): Minimum absolute correlation (0 to 1). Returns: dict: Highly correlated feature pairs and their correlation values.

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": "find_high_correlations"
  }
}

Input Schema

NameRequiredDescriptionDefault
thresholdNo
dataset_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It does reveal the return shape (pairs plus correlation values) and the numeric-only scope, but says nothing about whether the operation is read-only, computational cost on wide datasets, how missing values are handled, or what the default threshold does.

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 docstring-style Args/Returns layout is compact, front-loaded with the purpose, and every line carries information. There is no filler, though the format is mechanical rather than prose-optimized for an agent.

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 simple two-parameter read tool with no output schema and no annotations, the description covers purpose, inputs, and return value adequately. It still leaves open the default threshold behavior and the numeric-only prerequisite, which matter for correct invocation.

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 description coverage is 0%, so the description must compensate; it does document both parameters, explaining dataset_id as the dataset identifier and threshold as the minimum absolute correlation in the 0 to 1 range. However, it omits the schema's default of 0.8 and does not explain boundary or out-of-range behavior, leaving a meaningful gap.

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: 'Find highly correlated pairs of numerical features,' which tells the agent exactly what is computed and that only numerical features are considered. It does not distinguish itself from plausible siblings like analyze_correlation or find_target_relationships, so an agent must infer which correlation tool to pick.

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

Usage Guidelines2/5

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

There is no guidance on when to use this tool versus the many other correlation/relationship analysis siblings, nor any prerequisites such as the dataset needing numeric columns. Usage must be inferred entirely from the name and the one-line purpose.

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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