data-analysis-agent

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create_visualization

Generate charts from dataset sessions to reveal patterns. Supports histogram, scatter, bar, box, line, heatmap, pie, area, violin, and density plots with grouping/aggregation options.

Instructions

Generate a chart visualization from the dataset session. Supports chart types: 'histogram', 'scatter', 'bar', 'box', 'line', 'heatmap', 'pie', 'area', 'violin', 'density'. Parameters: x (horizontal column), y (vertical column), group_by (color/hue dimension), aggregation ('mean', 'sum', 'count', 'median', 'min', 'max'), bins (for histogram), columns (list of numeric cols for correlation heatmap), title (custom title), embed_thumbnail (default true). Returns the complete base64 image inline in 'thumbnail_b64' and saved file path. Render the inline image immediately to the user — DO NOT run bash or shell commands to locate or move the file.

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": {
      "session_id": "<session_id>",
      "chart_type": "<chart_type>"
    },
    "name": "create_visualization"
  }
}

Input Schema

Name	Required	Default
`x`	No
`y`	No
`bins`	No
`title`	No
`sample`	No
`columns`	No
`group_by`	No
`chart_type`	Yes
`session_id`	Yes
`aggregation`	No	mean
`embed_thumbnail`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 4, 2026

TDQS

A4.1/5.0

Behavior4/5

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

With no annotations provided, the description carries the full disclosure burden and does well: it reveals the return shape ('complete base64 image inline in thumbnail_b64 and saved file path') and warns against a specific failure mode ('DO NOT run bash or shell commands to locate or move the file'). It does not cover cost/performance traits of the sample parameter, but the key operational behaviors are disclosed.

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 description is dense but efficiently structured: purpose first, then a compact parameter reference, then return format and the critical behavioral warning at the end. The parameter list is somewhat long, but each element carries distinct information that the 0%-coverage schema fails to provide.

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?

Given 11 parameters, no annotations, and no output schema, the description covers chart-type options, most parameter semantics, return format, and a behavioral warning. Remaining gaps are per-chart-type parameter requirements (e.g., which params are needed for scatter vs heatmap) and the meaning of 'sample' — notable but not crippling for a visualization tool.

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 explains 8 of 11 parameters with semantic meaning: x as horizontal column, y as vertical column, group_by as color/hue dimension, bins for histograms, columns for correlation heatmaps. However, 'sample' (default 50000) is never explained, leaving an important numeric parameter undocumented.

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 opens with a specific verb and resource: 'Generate a chart visualization from the dataset session.' It enumerates ten supported chart types and clearly differentiates itself from all 22 siblings, none of which are visualization tools. An agent can immediately identify this as the only chart-creation tool in the set.

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 phrase 'from the dataset session' implies the tool requires a prior load_dataset call and a valid session_id, but no explicit when-to-use guidance, exclusions, or alternatives are stated. Usage context is inferable from the tool's unique role among siblings rather than from direct instruction.

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