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plot_distribution

Generate a distribution plot comparing one column across datasets, returning a base64 PNG and automatically selecting a bar or KDE plot.

Instructions

Generate distribution plot comparing datasets for a specific column. Returns base64-encoded PNG image. Automatically chooses bar plot (categorical) or KDE plot (numerical).

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_ids": "<dataset_ids>",
      "column_name": "<column_name>"
    },
    "name": "plot_distribution"
  }
}

Input Schema

Name	Required	Description	Default
`column_name`	Yes	Column name to plot distribution for
`dataset_ids`	Yes	List of dataset IDs to compare (e.g., [real_dataset_id, synthetic_dataset_id])

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Oct 16, 2026

TDQS

A3.8/5.0

Behavior4/5

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

With no annotations, the description carries the full burden and does meaningful work: it discloses the return format (base64-encoded PNG) and the automatic selection logic between bar (categorical) and KDE (numerical), letting the agent predict the output. It omits any statement of side effects, permissions, or size/time limits, keeping it out of 5 territory.

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?

Three short sentences, no filler, and the core purpose is front-loaded ahead of the return format and plot-type behavior. Every sentence earns its place.

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?

With no output schema, the description correctly fills that gap by stating the return type (base64 PNG), and the required parameters are covered by the schema. It is nearly complete, lacking only edge-case behavior such as what happens with unsupported column types or large datasets.

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 coverage is 100%, so both parameters (column_name, dataset_ids) are already documented in the schema, including the real-vs-synthetic example. The description adds only the notion of cross-dataset comparison, which is marginal beyond what the schema provides.

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 distribution plot') plus scope ('comparing datasets for a specific column'), so an agent knows exactly what comes back. It stops short of distinguishing itself from the related sibling get_marginal_distribution_score (plot vs. score), which would have made it 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?

Usage is implied by 'comparing datasets for a specific column', which signals the comparison scenario, but there is no explicit when-to-use, when-not-to-use, or named alternative (e.g., use get_marginal_distribution_score for a numeric score instead of a visual).

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