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AI-ML-Lab-MCP

AI-ML-Lab-MCP

by SyedHassan904
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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Fill Missing Mean

fill_missing_mean

Replace missing numerical values with the column mean to clean datasets; process one specified column or fill all numerical columns and return counts of filled values.

Instructions

Replace missing numerical values with the column mean. Args: dataset_id (str): ID of the dataset. col_name (str, optional): Only this column is filled. If omitted, all numerical columns are processed. Returns: dict: how many values were filled in each column.

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": "fill_missing_mean"
  }
}

Input Schema

NameRequiredDescriptionDefault
col_nameNo
dataset_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.9/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses the return value ('how many values were filled in each column'), which is genuinely useful, but omits whether the dataset is modified in place or a copy is created, and what happens with non-numerical columns or an unknown col_name.

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 action in one sentence, then compact Args/Returns sections with no filler. The docstring formatting is slightly verbose but every line carries information an agent needs.

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 two-parameter mutating tool with no output schema, the description covers the action, both parameters, and the shape of the return value. The remaining gap is in-place vs copy semantics, which matters for a tool that alters a dataset.

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% and the schema only declares types and a null default, so the description must compensate. It does: dataset_id is identified as the dataset ID and col_name is explained as an optional single-column filter whose omission triggers all numerical columns, adding real meaning beyond the schema.

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 verb and resource ('Replace missing numerical values with the column mean') and the mean qualifier implicitly separates it from fill_missing_median and fill_missing_mode siblings. An agent knows exactly what operation is performed.

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?

The description explains the col_name omitted case ('all numerical columns are processed'), which gives usage context, but never says when to prefer mean imputation over median, mode, or drop_missing, nor any prerequisites about dataset type or numeric-only columns.

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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