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AI-ML-Lab-MCP

AI-ML-Lab-MCP

by SyedHassan904
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Get Dataset Metadata

get_dataset_metadata

Retrieve row and column counts and column names for a registered dataset to inspect its structure before analysis or cleaning.

Instructions

Retrieve metadata of a registered dataset: number of rows and columns and the column names. Args: dataset_id (str): ID of the dataset. Returns: dict: dataset_id, rows, columns and column_names.

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": "get_dataset_metadata"
  }
}

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 burden. It does disclose the return payload keys, which is valuable given there is no output schema, but it never states that this is a read-only operation, whether it requires the dataset to be active in memory, or how missing/invalid IDs are reported.

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 purpose sentence followed by compact Args/Returns blocks; nothing is padded. Minor waste in repeating the dataset_id name and type already present in the schema.

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 with no output schema, documenting the return keys covers the main gap. Still missing are error behavior for invalid IDs and any signal distinguishing it from sibling metadata/shape tools.

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% and the single parameter is only restated as 'dataset_id (str): ID of the dataset', adding type but no format, origin, or how the ID is obtained. It confirms the parameter is required-but-simple without compensating for the empty schema documentation.

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?

Specific verb+resource: 'Retrieve metadata of a registered dataset', with the returned fields enumerated (rows, columns, column names). It does not differentiate itself from nearby siblings such as dataset_info, dataset_shape, or describe_dataset, which plausibly 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 tool versus alternatives, no prerequisites (e.g., dataset must be registered/loaded), and no note about what happens with an unknown ID. Usage is only implied by the phrase 'registered dataset'.

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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