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AI-ML-Lab-MCP

AI-ML-Lab-MCP

by SyedHassan904
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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Rename Column

rename_column

Rename a dataset column by specifying its dataset ID, current column name, and new name, returning the dataset ID and both column names.

Instructions

Rename a column in the dataset. Args: dataset_id (str): ID of the dataset. column_name (str): Current column name. new_name (str): New column name. Returns: dict: Dataset ID and old/new 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>",
      "column_name": "<column_name>",
      "new_name": "<new_name>"
    },
    "name": "rename_column"
  }
}

Input Schema

NameRequiredDescriptionDefault
new_nameYes
dataset_idYes
column_nameYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.9/5.0
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 of behavioral disclosure. It implies a mutation but says nothing about permissions, reversibility, collisions with existing column names, or whether downstream data is affected; the only behavioral hint is the Returns line naming dataset ID and old/new column names.

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 docstring is short, front-loaded with the action, and then cleanly separates Args and Returns. The Args section is mildly redundant with a self-describing schema but is not wasteful.

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 simple three-parameter mutation with no output schema, the description covers the parameters and the return shape, which is adequate. It omits the edge cases and failure modes that matter most for a rename operation, leaving the agent to guess at error behavior.

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 description coverage is 0%, so the description must compensate. It documents all three parameters (dataset_id as the dataset identifier, column_name as the current name, new_name as the target), but these restate the parameter names and add little meaning such as naming constraints, case sensitivity, or character rules.

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 opens with a specific verb and resource: 'Rename a column in the dataset.' An agent can distinguish this from sibling operations like drop_column or convert_data_types without opening the schema. It stops short of naming an explicit alternative, so it is a 4 rather than a 5.

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 prerequisites, and no exclusions. Nothing states what happens if new_name already exists, whether this fails on a duplicate, or how it relates to sibling column tools such as drop_column or column_types.

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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