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generate_bar_chart

Generate a bar chart from dataset columns to visualize aggregated values. Specify X-axis, Y-axis, optional grouping, and get the chart file path and data.

Instructions

    Generate a bar chart from a dataset column.

    Args:
        dataset:   Name of the dataset (e.g. "sales", "users").
        x_column:  Column to use for the X axis (categorical or date).
        y_column:  Numeric column to aggregate on the Y axis (sum by default).
        title:     Chart title shown at the top.
        group_by:  Optional column to group bars by colour (e.g. "category").
        filename:  Output filename (auto-generated if empty).

    Returns:
        A JSON str with {"chart_path":"...","chart_data":"..."}.

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": "<dataset>",
      "x_column": "<x_column>",
      "y_column": "<y_column>",
      "title": "<title>"
    },
    "name": "generate_bar_chart"
  }
}

Input Schema

Name	Required	Description	Default
`title`	Yes
`dataset`	Yes
`filename`	No
`group_by`	No
`x_column`	Yes
`y_column`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 30, 2026

TDQS

A4.3/5.0

Behavior4/5

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

No annotations are present, so the description carries the full behavioral burden. It discloses the default aggregation ('sum by default'), auto-generated filename behavior, and exact return format ('JSON str with chart_path and chart_data'). It does not mention filesystem side effects or dataset prerequisites, but these omissions are minor for a chart-generation tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The docstring is tightly structured with a one-line summary, grouped Args, and a Returns section. Every line contributes needed information, and there is no redundant or vague 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 6-parameter tool with no annotations, the description is nearly self-sufficient: it covers all parameters, defaults, optional behavior, and the output format. It lacks guidance on selecting this tool among siblings and explicit error/prerequisite conditions, which prevents a perfect score.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, and the description compensates fully. Each parameter is explained with type constraints (categorical/date for x_column, numeric for y_column), defaults (sum aggregation, auto-generated filename), and optionality (group_by). This adds meaningful semantics beyond the raw schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('Generate') and resource ('bar chart') and defines the source as a dataset column. The x_column/y_column parameter explanations clarify the categorical vs numeric mapping, which distinguishes it from sibling chart generators such as pie, histogram, line, and scatter.

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?

No explicit when-to-use or alternative-selection guidance is provided. The name and parameters imply it is for comparing categorical or date categories against a numeric aggregate, but the description never tells an agent when to choose this over generate_pie_chart, generate_histogram, or generate_line_chart.

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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