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AI-ML-Lab-MCP

AI-ML-Lab-MCP

by SyedHassan904
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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Dataset Info

dataset_info

Returns a summary of a dataset's structure, dimensions, column types, missing values, and numerical statistics so you can inspect data before analysis.

Instructions

Return a comprehensive summary of dataset structure, dimensions, types, and missing values. Args: dataset_id (str): ID of the dataset. Returns: dict: rows, columns, column_names, datatypes_of_columns, no_of_null_in_each_column and summary_of_numerical_columns.

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": "dataset_info"
  }
}

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 operation is a non-mutating read whose output includes rows, columns, dtypes, null counts, and numeric summaries. It says nothing about failure modes for an invalid dataset_id, cost on large datasets, or whether the summary is computed on the fly.

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 first sentence front-loads the purpose and the Args/Returns blocks are compact and clearly labeled. The docstring formatting is slightly mechanical for a description field, but no sentence is wasted.

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?

Since no output schema exists, the enumerated return keys are genuinely necessary and are supplied. For a single-parameter read tool that is nearly sufficient; only error behavior and the relationship to overlapping summary siblings are unaddressed.

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, and it only partially does: it labels dataset_id as the 'ID of the dataset' without saying where the ID comes from, whether it is a name or a UUID, or how to discover valid values. This is the minimum viable labeling for a single required parameter.

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 uses a specific verb ('Return') and resource ('dataset structure') and enumerates exactly what the summary covers: dimensions, types, missing values, plus the return keys. An agent knows precisely what it gets. It stops short of 5 because it never distinguishes itself from close siblings like dataset_shape, describe_dataset, column_types, or get_dataset_metadata.

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, no prerequisites (e.g. the dataset must be loaded), and no mention of the overlapping siblings. The only implied usage is 'get an overview', which an agent must infer from the enumerated return fields.

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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