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Python
Remote

Plot Polar Chart

plot_polar_chart
Read-onlyIdempotent

Generate a polar area chart directly from a Teradata table by specifying the table, category label column, and numeric value column. Use this when an explicit polar chart is requested.

Instructions

Generate a polar area chart that reads directly from a Teradata table — do NOT use base_readQuery first. Specify the table in table_name, the category column in labels, and the numeric value column in column. Use when the user explicitly asks for a polar chart or polar area chart. For standard pie-style breakdowns, use plot_pie_chart instead.

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

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

column:
    Required Argument.
    Specifies the numeric value column for the polar chart.
    Types: str

RETURNS: dict

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
columnYes Required Argument. Specifies the numeric value column for the polar chart. Types: str
labelsYes Required Argument. Specifies the category column for labels. Types: str
table_nameYes Required Argument. Specifies the name of the table to generate the polar chart. Types: str

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changedv1.0.1
    • changedInput schema / properties / column / description
      Previous value: -"\nRequired Argument.\nSpecifies the column to be used for generating the line plot.\nTypes: str"New value: +"\nRequired Argument.\nSpecifies the numeric value column for the polar chart.\nTypes: 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 polar chart.\nTypes: str"
  2. Changed7 schema fields changedv0.2.1
    • addedInput schema / additionalProperties
      Added value: +false
    • addedInput schema / properties / column / description
      Added value: +"\nRequired Argument.\nSpecifies the column to be used for generating the line plot.\nTypes: str"
    • removedInput schema / properties / column / title
      Removed value: -"Column"
    • 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, so the safety profile is covered. The description adds meaningful behavioral context by stating the tool reads directly from a Teradata table and must not be preceded by base_readQuery, which is a non-obvious integration detail beyond the annotations.

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: it states the action, the critical negative instruction, the parameter mapping, the intended trigger context, and the sibling alternative in a few sentences. Every sentence earns its place with no filler or repetition.

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?

For a simple chart tool with three required string parameters, read-only annotations, and no output schema, the description covers what the tool does, how to call it, when to use it, and what not to call first. Nothing essential is missing for correct invocation.

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 from table_name, labels, and column to their roles, but adds no additional syntax or format details beyond what the schema provides. 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?

The description opens with a specific verb and resource: "Generate a polar area chart that reads directly from a Teradata table." It clearly distinguishes itself from plot_pie_chart and explicitly warns against using base_readQuery first, so an agent can identify the correct tool without opening schemas.

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?

The description gives explicit when-to-use guidance: "Use when the user explicitly asks for a polar chart or polar area chart." It also names an alternative for a different case: "For standard pie-style breakdowns, use plot_pie_chart instead," and states the do-not-do action of avoiding base_readQuery.

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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