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AI-ML-Lab-MCP

AI-ML-Lab-MCP

by SyedHassan904
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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Handle Missing Values

handle_missing_values

Drops dataset rows with up to 5% missing values and columns with 60% or more missing values, leaving moderate missingness unchanged for separate imputation.

Instructions

Automatically handle missing values based on their percentage.

  • up to 5% missing: drop the rows that contain the missing values

  • 60% or more missing: drop the column

  • 5%-60%: leave unchanged (use the fill_missing_* tools or impute_missing) Args: dataset_id (str): ID of the dataset. Returns: dict: dropped columns, the columns used for row removal and the row counts.

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": "handle_missing_values"
  }
}

Input Schema

NameRequiredDescriptionDefault
dataset_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.4/5.0
Behavior4/5

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 that this is a destructive operation (row and column deletion) with deterministic thresholds, plus what the return payload contains. The notable gap is whether the dataset is mutated in place or a copy is produced — a material omission for a destructive tool, and reversibility is unstated.

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?

Front-loaded purpose followed by a scannable threshold list, then explicit Args/Returns blocks. Every line conveys decision-relevant information; nothing is padding.

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?

No output schema exists, but the description summarizes the return value (dropped columns, row-removal columns, row counts), so return semantics are covered. The remaining gap is mutation semantics — whether the original dataset is modified or a new one is returned — which matters given the destructive thresholds.

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?

Only one parameter, and schema coverage is 0%, so the description must compensate. It identifies dataset_id as the dataset identifier, which is slightly better than the bare schema but adds no format, source, or lookup guidance (e.g. where to obtain the ID).

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?

States a specific operation (automatic missing-value handling) and enumerates the exact decision rules with numeric thresholds (<=5% drop rows, >=60% drop column, 5-60% unchanged). It also names the sibling tools (fill_missing_*, impute_missing) that cover the untouched case, so an agent can distinguish it from them without opening any schema.

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

Usage Guidelines5/5

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

Gives explicit when-to-use behavior via the percentage bands and explicitly redirects the 5-60% case to fill_missing_* or impute_missing. Both the 'use this' and 'use something else' conditions are stated rather than inferred.

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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