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AI-ML-Lab-MCP

AI-ML-Lab-MCP

by SyedHassan904
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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Unique Values

unique_values

Retrieve distinct values from a chosen dataset column to inspect categories, validate data, or prepare cleaning and analysis steps.

Instructions

Return unique values for a selected column. Args: dataset_id (str): ID of the dataset. col_name (str): Name of the column. Returns: dict: unique_values, column_name and dataset_id.

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>",
      "col_name": "<col_name>"
    },
    "name": "unique_values"
  }
}

Input Schema

NameRequiredDescriptionDefault
col_nameYes
dataset_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.2/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden. It is implicitly a safe read operation and it does disclose the return structure (unique_values, column_name, dataset_id), but it says nothing about behavior on missing values, large-cardinality columns, or failure cases such as an unknown dataset_id or col_name.

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?

Short and front-loaded, with the purpose sentence first and the Args/Returns block supporting it. The docstring-style scaffolding is slightly heavier than needed for two string parameters, but nothing is wasted or repetitive.

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 simple two-parameter read tool with no output schema, the definition covers the inputs and names the three return keys, which is most of what an agent needs. The main gap is behavioral edge cases, which matters less here because there is no mutation risk.

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 does name both parameters and their intent (dataset ID, column name) plus the return keys. However, it only restates the parameter names in prose and adds no format, constraint, or validation detail (e.g., whether col_name must exist or match case) beyond the bare types.

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?

Description gives a specific verb and resource: "Return unique values for a selected column", which is concrete enough for an agent to know exactly what it produces. It does not differentiate against potentially overlapping siblings such as analyze_categorical or column_types, so it stops short of 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 or when-not-to-use guidance, no mention of alternatives, and no prerequisites (e.g., that the dataset must already be loaded). The agent must infer the tool's role purely from its name and one-line purpose.

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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