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AI-ML-Lab-MCP

AI-ML-Lab-MCP

by SyedHassan904
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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Dataset Shape

dataset_shape

Retrieve the number of rows and columns for a dataset by its ID to quickly assess its dimensions and structure.

Instructions

Return the number of rows and columns. Args: dataset_id (str): ID of the dataset. Returns: dict: rows and 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_shape"
  }
}

Input Schema

NameRequiredDescriptionDefault
dataset_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.8/5.0
Behavior2/5

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 implies a read-only operation and hints at the return shape, but says nothing about error behavior for an invalid dataset_id, permissions, cost on large datasets, or whether the result is exact or sampled.

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 core sentence is front-loaded and the whole definition is short. The 'Args:'/'Returns:' scaffolding adds a little boilerplate, but nothing is padded or repetitive.

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 single-parameter read tool with no output schema, the description covers the essential purpose and roughly what comes back. It is minimal but adequate; the missing error and identifier-format details are the main gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must supply parameter meaning. It only offers 'ID of the dataset', which essentially restates the parameter name; there is no note on accepted format, source, or how to obtain a valid ID.

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 states a specific verb and result ('Return the number of rows and columns'), so an agent knows this yields dataset dimensions. It is clear on its own, but it never distinguishes itself from look-alike siblings such as dataset_info, describe_dataset, 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 when-to-use guidance, no prerequisite (e.g. dataset must exist/be loaded), and no mention of when a sibling like dataset_info would be preferable. The agent must infer the usage context from the tool name alone.

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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