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AI-ML-Lab-MCP

AI-ML-Lab-MCP

by SyedHassan904
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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Copy Dataset

copy_dataset

Save a dataset under a new ID so cleaning or experiments never alter the original; the copy can be modified independently.

Instructions

Save a copy of a dataset under a new id, e.g. before experimenting with cleaning steps. The original is left untouched and the copy can be changed independently. Args: dataset_id (str): ID of the dataset to copy. Returns: dict: dataset_id, rows, columns and column_names of the new copy.

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": "copy_dataset"
  }
}

Input Schema

NameRequiredDescriptionDefault
dataset_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full behavioral burden and does disclose the key traits: the original is left untouched and the copy mutates independently, plus the shape of the returned metadata. It omits practical behavior such as memory/storage cost of duplicating a large dataset, whether the copy is deep or shallow, and error conditions for an invalid id.

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?

Purpose and the non-destructive guarantee are front-loaded in the first two sentences, with Args/Returns following in a conventional block. The Args section is slightly ceremonial for one already self-evident parameter but costs little.

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 one-parameter tool with no annotations and no output schema, the description covers purpose, side-effect semantics, and the returned fields, which is nearly everything needed to call it correctly. Only large-dataset cost and failure behavior remain unaddressed.

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

Parameters4/5

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 does document the single parameter as the ID of the dataset to copy. The semantics match the sole required string parameter, though no format example or valid-id source is given.

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 ('Save a copy of a dataset under a new id'), so the operation is unambiguous. It does not name or contrast with sibling operations such as export_dataset or delete_dataset, so differentiation is left to the agent.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides one concrete motivating scenario ('before experimenting with cleaning steps'), which implies when to use it. There is no explicit when-not guidance and no named alternative for cases where a copy is not needed.

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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