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AI-ML-Lab-MCP

AI-ML-Lab-MCP

by SyedHassan904
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Get Split Info

get_split_info

Retrieves the current state of a train/test split, including shapes, feature columns, data types, missing values, and preprocessing steps applied so far.

Instructions

Show the current state of a train/test split: shapes, feature columns, data types, missing values and the preprocessing steps applied so far. Args: split_id (str): ID of the split created by train_test_split. Returns: dict: Split details.

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": {
      "split_id": "<split_id>"
    },
    "name": "get_split_info"
  }
}

Input Schema

NameRequiredDescriptionDefault
split_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.7/5.0
Behavior3/5

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

With no annotations, the description carries the full burden; 'show' correctly signals a read-only, non-mutating inspection and it discloses what information is returned. However, it says nothing about behavior on an invalid or unknown split_id, whether preprocessing state reflects only applied steps, or any limits on the report.

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?

Front-loaded summary sentence carries the substance, followed by compact Args/Returns lines with no filler. Nothing in it is redundant or padded.

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 read tool with no output schema and no annotations, the description covers what is returned field-by-field, which is the key information an agent needs. It leaves only edge-case behavior (invalid IDs, error mode) 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; the Args block does define split_id as a string and, importantly, states its provenance ('created by train_test_split'), which the bare schema does not convey. It stops short of format examples or where such IDs are obtained.

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?

Uses a specific verb and resource ('show the current state of a train/test split') and enumerates exactly what is reported: shapes, feature columns, data types, missing values and preprocessing steps. It implicitly separates itself from the sibling train_test_split by saying the ID must come from a split 'created by train_test_split', though it does not explicitly contrast with alternatives like dataset_info.

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 is only implied: the reader infers this is for inspecting a split after creation or before modeling. There is no explicit when-to-use/when-not statement and no named alternative (e.g., dataset_info for whole-dataset summaries), so an agent must reason about selection itself.

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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