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manzoor-source

Teradata MCP Server

plot_radar_chart

Read-onlyIdempotent

Generate a radar chart directly from a Teradata table for multi-dimensional comparison across categories. Specify table, label column, and value columns.

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
Behavior5/5

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

Adds important behavioral detail beyond readOnly and idempotent annotations: it reads directly from a table and advises not to pre-fetch with base_readQuery. No contradiction with 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?

Concise and well-structured. One short paragraph with key purpose, usage guidance, and parameter mapping. No wasted words.

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?

Lacks details on return value format (only says 'dict' in a comment). With no output schema, description could specify what the dict contains (e.g., chart object). Otherwise sufficient given annotations and sibling context.

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% and description repeats parameter info almost verbatim. Adds minor context in the main paragraph (e.g., 'one or more value columns'), but no additional semantics or constraints beyond the schema.

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 clearly states it generates a radar chart from a Teradata table, specifies the verb 'generate' and resource, and differentiates from sibling tools like plot_line_chart by stating when to use alternatively.

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 when-to-use (spider chart, radar chart, multi-dimensional comparison) and when-not-to-use (time-series/trend data, directing to plot_line_chart). Also warns against using base_readQuery first.

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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