Skip to main content
Glama
Sign In
AI-ML-Lab-MCP

AI-ML-Lab-MCP

by SyedHassan904
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Auto Visualize

auto_visualize

Automatically selects and saves suitable charts for a dataset—histograms, category counts, correlation heatmaps, and target-focused plots—so you can explore data quickly.

Instructions

Automatically choose and save appropriate charts for a dataset:

  • histograms of numerical columns

  • count charts of low-cardinality categorical columns

  • correlation heatmap (2+ numerical columns)

  • target column charts (scatter plots of the most correlated features for a numerical target, class balance for a categorical target) Args: dataset_id (str): ID of the dataset. target_column (str, optional): Target column to focus the charts on. Returns: dict: Dataset ID, the charts created (each with a reason), and skipped columns.

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": "auto_visualize"
  }
}

Input Schema

NameRequiredDescriptionDefault
dataset_idYes
target_columnNo

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 exist, so the description carries the full burden. It usefully discloses the return payload (dataset ID, charts created each with a reason, and skipped columns) and the implicit side effect that charts are saved, but never says where artifacts are written, what format, whether prior outputs are overwritten, or what happens to unsupported column types.

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 behavior list is front-loaded and each bullet earns its place by describing a distinct chart-generation rule. The Args/Returns sections are compact, though the formatting is boilerplate rather than tailored prose.

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 two-parameter tool with no annotations and no output schema, the definition covers inputs, generated outputs, and return shape well enough to call it confidently. The remaining gap is the destination/lifecycle of the saved chart files, which an agent would still need to discover.

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 documents both parameters plus their effects: dataset_id identifies the dataset, and target_column focuses charts, with concrete behavioral consequences (scatter plots of most correlated features for numerical targets, class balance for categorical). Only the optional/default nature and acceptable column types are left slightly fuzzy.

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 phrase ('Automatically choose and save appropriate charts') plus an enumerated list of the chart types produced, which clearly separates it from single-chart siblings like heatmap, countplot, or boxplot. It stops short of explicitly naming an alternative tool, but the 'automatically choose' framing makes the distinction inferable.

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?

The chart-selection rules (histograms for numerical, count charts for low-cardinality categorical, heatmap for 2+ numerical, target charts) imply when the tool is useful, e.g. exploratory profiling. However there is no explicit when-to-use clause, no when-not-to-use, and no routing to the individual plotting siblings for a single specific chart.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server

Other Tools