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AI-ML-Lab-MCP

AI-ML-Lab-MCP

by SyedHassan904
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Missing Values

missing_values

Detect and summarize missing values by column in a dataset, returning counts per column to guide data cleaning.

Instructions

Detect and summarize missing values by column. Args: dataset_id (str): ID of the dataset. Returns: dict: null_values_in_columns (column name -> number of missing 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": "missing_values"
  }
}

Input Schema

NameRequiredDescriptionDefault
dataset_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.2/5.0
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 it does disclose the return payload shape (column name -> count). However it never states that the operation is non-destructive/read-only, nor what happens with an invalid dataset_id, so key behavioral traits remain unstated.

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 purpose in one sentence, then Args/Returns blocks. The docstring scaffolding is slightly verbose but every line carries some information and there is no filler.

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 single-parameter diagnostic tool with no output schema, the description covers the input, the operation, and the returned structure, which is nearly everything an agent needs to call it correctly. Only the read-only contract and error behavior are missing.

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 coverage is 0%, so the description must compensate, and it only restates the parameter as 'ID of the dataset' — essentially a tautology with no format, source, or lookup hints (e.g., how to obtain an ID via dataset_info). Minimal compensation for the coverage 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?

States a specific verb pair ('Detect and summarize') and resource ('missing values by column'), which is enough to distinguish this diagnostic tool from the mutating siblings. It stops short of naming handle_missing_values or drop_missing as the contrasting write-side alternatives.

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 explicit when-to-use or when-not-to-use guidance. An agent must infer that this is the read-only inspection step that precedes fill_missing_* / drop_missing tools; nothing in the text says so or rules out any alternative.

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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