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Python
Remote

Plot Radar Chart

plot_radar_chart
Read-onlyIdempotent

Create a radar chart directly from a Teradata table. Specify the table, category column, and one or more value columns to generate a multi-dimensional comparison without pre-fetching data.

Instructions

Generate a radar chart (spider chart or web chart) that reads directly from a Teradata table — do NOT use base_readQuery to pre-fetch data first. Specify the table in table_name, the category column in labels, and one or more value columns in columns. Use when the user asks for a spider chart, radar chart, web chart, or multi-dimensional comparison across categories. For time-series or trend data, use plot_line_chart instead.

PARAMETERS: table_name: Required Argument. Specifies the name of the table to generate the radar chart. Types: str

labels:
    Required Argument.
    Specifies the category column for labels.
    Types: str

columns:
    Required Argument.
    Specifies the value column(s) for the radar chart.
    Types: str | List[str]

RETURNS: dict

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
labelsYes Required Argument. Specifies the category column for labels. Types: str
columnsYes Required Argument. Specifies the value column(s) for the radar chart. Types: str | List[str]
table_nameYes Required Argument. Specifies the name of the table to generate the radar chart. Types: str

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changedv1.0.1
    • changedInput schema / properties / columns / description
      Previous value: -"\nRequired Argument.\nSpecifies the column to be used for generating the line plot.\nTypes: str"New value: +"\nRequired Argument.\nSpecifies the value column(s) for the radar chart.\nTypes: str | List[str]"
    • changedInput schema / properties / labels / description
      Previous value: -"\nRequired Argument.\nSpecifies the labels to be used for the line plot.\nTypes: str"New value: +"\nRequired Argument.\nSpecifies the category column for labels.\nTypes: str"
    • changedInput schema / properties / table_name / description
      Previous value: -"\nRequired Argument.\nSpecifies the name of the table to generate the donut plot.\nTypes: str"New value: +"\nRequired Argument.\nSpecifies the name of the table to generate the radar chart.\nTypes: str"
  2. Changed7 schema fields changedv0.2.1
    • addedInput schema / additionalProperties
      Added value: +false
    • addedInput schema / properties / columns / description
      Added value: +"\nRequired Argument.\nSpecifies the column to be used for generating the line plot.\nTypes: str"
    • removedInput schema / properties / columns / title
      Removed value: -"Columns"
    • addedInput schema / properties / labels / description
      Added value: +"\nRequired Argument.\nSpecifies the labels to be used for the line plot.\nTypes: str"
    • removedInput schema / properties / labels / title
      Removed value: -"Labels"
    • addedInput schema / properties / table_name / description
      Added value: +"\nRequired Argument.\nSpecifies the name of the table to generate the donut plot.\nTypes: str"
    • removedInput schema / properties / table_name / title
      Removed value: -"Table Name"
  3. Addedv1.0.0

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true. The description adds a meaningful behavioral constraint: it reads directly from a Teradata table and explicitly warns 'do NOT use base_readQuery to pre-fetch data first.' This goes beyond the annotations and helps the agent avoid an incorrect invocation pattern.

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 compact and front-loaded with the core purpose, immediately followed by the critical integration constraint and the usage guideline. Every sentence adds operational value; there is no filler or repetition of schema details beyond what is useful.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool is simple with three documented parameters and safe read-only annotations. The description covers when to use it, what it reads, and how to invoke it, and it states the return type as dict. Nothing essential is missing for an agent to call it correctly.

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 description coverage is 100%, so the schema already documents all three parameters. The description restates the mapping ('table_name, labels, columns') and adds that columns can be one or more value columns, but this adds little meaning beyond what the anyOf schema type already conveys. Baseline 3 is appropriate.

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?

States a specific verb plus resource: 'Generate a radar chart... that reads directly from a Teradata table.' It also names synonyms (spider/web chart) and clearly distinguishes it from plot_line_chart and other plotting siblings. An agent can tell exactly what this 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 Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit selection criteria: 'Use when the user asks for a spider chart, radar chart, web chart, or multi-dimensional comparison across categories.' It also names the alternative for time-series or trend data (plot_line_chart), giving a clear when-to-use versus when-not-to-use distinction.

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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