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generate_histogram

Create a histogram to visualize the distribution of a numeric column in a dataset. Returns chart path and data for summary reports.

Instructions

    Generate a histogram showing the distribution of a numeric column.

    Args:
        dataset:  Name of the dataset.
        column:   Numeric column to plot.
        title:    Chart title.
        bins:     Number of bins (default 20).
        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>",
      "column": "<column>",
      "title": "<title>"
    },
    "name": "generate_histogram"
  }
}

Input Schema

Name	Required	Description	Default
`bins`	No
`title`	Yes
`column`	Yes
`dataset`	Yes
`filename`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 30, 2026

TDQS

A4.1/5.0

Behavior3/5

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

With no annotations, the description carries the behavioral burden; it discloses the JSON return shape and filename auto-generation. It does not state whether an existing file is overwritten, whether charts are persisted to disk as a side effect, or what error behavior occurs for non-numeric columns, though filename and chart_path imply file output.

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 description is front-loaded with the core purpose and uses a compact Args/Returns structure where every line contributes parameter or output information. There is no filler or repetition.

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 five-parameter plotting tool, the description covers every parameter and the return shape, which is sufficient for calling it. It is slightly incomplete on higher-level context such as prerequisites (dataset must exist) and sibling selection, but the output schema signal and parameter coverage reduce the impact of that gap.

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%, but the Args section gives meaningful semantic descriptions for all five parameters, including that column must be numeric, the bins default, and the filename auto-generation behavior. This fully compensates for the absence of schema descriptions.

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 first sentence clearly states the action (generate), the artifact (histogram), and the input condition (numeric column), which inherently differentiates it from pie/bar/line/scatter siblings. The chart type and numeric qualifier leave no ambiguity about what the tool does.

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 numeric-column/distribution phrasing implies this tool is for inspecting continuous numeric distributions, but there is no explicit guidance about when to prefer it over generate_bar_chart, generate_line_chart, or generate_scatter_plot, and no mention of needing a dataset discovery step such as discover_datasets.

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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