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AI-ML-Lab-MCP

AI-ML-Lab-MCP

by SyedHassan904
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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List Columns

list_columns

Retrieve all column names from a dataset by its ID to inspect the schema and guide cleaning, exploration, or feature selection.

Instructions

Return all column names. Args: dataset_id (str): ID of the dataset. Returns: dict: 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": "list_columns"
  }
}

Input Schema

NameRequiredDescriptionDefault
dataset_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3/5.0
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 shape ('dict: column_names'), which is the key behavioral fact for this read-only listing operation, but says nothing about column ordering, behavior on an invalid or missing dataset_id, or whether names are qualified.

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 single-sentence purpose is front-loaded and there is no filler, though the raw 'Args:'/'Returns:' docstring scaffolding is slightly noisier than plain prose would be.

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, stating the purpose and return type is close to sufficient. The remaining gaps are the source/format of dataset_id and error behavior, which matter for correct invocation.

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 compensate. Instead it only restates the parameter name: 'dataset_id (str): ID of the dataset.' It adds no meaning about accepted formats, where the ID comes from (e.g. list_datasets), or failure modes.

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 resource: 'Return all column names.' An agent knows immediately what it gets. It does not distinguish itself from the sibling 'column_types', which presumably returns names plus types, so sibling differentiation is absent.

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 at all — no mention of when this is preferable to 'column_types' or other schema-inspection siblings, and no preconditions such as requiring the dataset to exist in the current session.

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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