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AI-ML-Lab-MCP

AI-ML-Lab-MCP

by SyedHassan904
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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Violinplot

violinplot

Generate and save violin plots to visualize numerical data distributions in a dataset, for one column or all numerical columns, with optional category grouping.

Instructions

Generate and save violin plots showing the distribution of numerical data. Args: dataset_id (str): ID of the dataset. column_name (str, optional): Numerical column to visualize. If omitted, generates violin plots for all numerical columns. group_by (str, optional): Column to split column_name by (top 10 groups). Requires column_name. Returns: dict: Dataset ID, columns/groups plotted, and saved image filename.

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": "violinplot"
  }
}

Input Schema

NameRequiredDescriptionDefault
group_byNo
dataset_idYes
column_nameNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior3/5

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

No annotations are supplied, so the description carries the full burden. It does disclose the key side effect (a saved image file) and names the returned keys, which is real value. It omits where/format the image is saved, overwrite behavior, and cost/compute expectations for plotting all numerical columns.

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?

Front-loaded one-sentence purpose followed by compact Args/Returns blocks; no filler. The docstring formatting is slightly verbose for a description field but each line carries required information.

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 3-parameter tool with no annotations and no output schema, the description covers purpose, all parameters and their interactions, and the return dictionary including the saved filename. What remains missing (storage location/format of the image, error behavior) is secondary but not trivial.

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 description coverage is 0%, so the description must compensate, and it does: all three parameters are explained with meaning and constraints, including that group_by splits column_name into the top 10 groups and requires column_name. Only minor gaps remain (e.g., accepted column types, what happens with a non-numerical column_name).

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+resource: 'Generate and save violin plots showing the distribution of numerical data.' Clear what the tool does and that a file artifact is produced. It does not explicitly differentiate itself from near-neighbors like boxplot or analyze_distribution, 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 Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives conditional behavior ('If omitted, generates violin plots for all numerical columns' and 'Requires column_name' for group_by), which implies default usage. However, there is no guidance on when to pick this over boxplot, countplot, or analyze_distribution, and no exclusions or prerequisites.

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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