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AI-ML-Lab-MCP

AI-ML-Lab-MCP

by SyedHassan904
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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Delete Dataset

delete_dataset

Remove a dataset from the current session while preserving any derived splits and models. Dataset IDs are not reused, preventing old references from pointing to a different dataset.

Instructions

Remove a dataset from the session. Splits and models already made from it are kept. Dataset ids are never reused, so old references cannot point at a different dataset. Args: dataset_id (str): ID of the dataset to delete. Returns: dict: The deleted id and the remaining dataset ids.

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": "delete_dataset"
  }
}

Input Schema

NameRequiredDescriptionDefault
dataset_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.8/5.0
Behavior4/5

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

With no annotations supplied, the description carries the full burden and does disclose genuinely non-obvious behavior: dependent splits and models are kept rather than cascaded, and dataset ids are never recycled so stale references cannot silently resolve to a new dataset. It stops short of stating error behavior for an unknown id or confirming that the removal cannot be undone.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two front-loaded sentences give the action and its consequences before the Args/Returns block, and the block itself is terse. No sentence is padding; each conveys a fact the agent would otherwise have to guess.

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 single-parameter destructive tool with no annotations and no output schema, the description covers what is removed, what survives, id-reuse semantics, and the return shape ('the deleted id and the remaining dataset ids'). The only gap is error and permission behavior for an invalid or inaccessible dataset id.

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%, so the description must carry the parameter, and its Args block only restates that dataset_id is a string identifying the dataset to delete. The 'ids are never reused' remark implies ids come from prior creation calls and remain stable references, which is the only real semantic added beyond the schema.

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: 'Remove a dataset from the session.' The scope qualifier ('from the session') plus the note that derived splits and models survive makes the effect concrete enough to distinguish it from read-only siblings like dataset_info or copy_dataset, though no sibling is named explicitly.

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?

Usage context is implied by the consequences described (derived artifacts are retained, ids are not reused), which helps an agent judge whether deletion is safe. There is no explicit when-to-use versus when-not-to-use guidance, no warning that deletion is irreversible, and no stated preconditions or alternative tools.

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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