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AI-ML-Lab-MCP

AI-ML-Lab-MCP

by SyedHassan904
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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Describe Dataset

describe_dataset

Generate statistical summaries for numerical and categorical columns of a dataset, including numeric statistics, unique value counts, and value frequencies for categorical fields.

Instructions

Generate statistical summaries for numerical and categorical columns. Args: dataset_id (str): ID of the dataset. Returns: dict: the numerical statistical summary, the number of unique values of each categorical column, and the unique values with their counts for each categorical 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": "describe_dataset"
  }
}

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?

No annotations are supplied, so the description carries the full burden; it does disclose the shape of the return value (numerical stats, unique counts, unique values with counts), which is genuinely useful behavioral information. It omits whether the operation is read-only, how large datasets are handled, or whether results are cached or paginated.

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 sentence is front-loaded and the docstring-style Args/Returns block is compact. Minor waste from the 'Args:/Returns:' boilerplate, but nothing substantive is filler.

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 one-parameter read tool, the return description makes it largely callable, and no output schema exists so the Returns block does real work. It remains incomplete on the read-only nature and on how it relates to the overlapping analysis siblings.

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% for the single required parameter, and the description only restates 'dataset_id (str): ID of the dataset', adding no format, source, or lookup guidance. It does at least name and explain the parameter, keeping it above the minimum.

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 ('Generate statistical summaries') and names the column types covered, which distinguishes it from pure metadata tools like dataset_info. It does not, however, differentiate itself from analyze_distribution or analyze_categorical, which appear to overlap.

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?

No guidance on when to use this versus the many analyze_* siblings or dataset_info; the reader must infer that this is a broad overview tool. There are no exclusions, prerequisites, or alternatives named.

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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