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Teradata

Teradata MCP Server

Official
by Teradata

plot_radar_chart

Read-onlyIdempotent

Generate a radar chart from a Teradata table for multi-dimensional category comparison. Use table name, 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
Behavior4/5

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

Annotations already provide readOnlyHint and idempotentHint. The description adds that it reads directly from a Teradata table and warns against pre-fetching, which is valuable behavioral context beyond annotations. However, it does not describe what the returned chart looks like.

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 very concise: two sentences plus a brief parameter overview. It front-loads the purpose and then provides usage guidance without any redundant or unnecessary information. Every sentence adds value.

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?

For a simple chart tool with 3 parameters and no output schema, the description covers purpose, usage, parameters, and an alternative. It lacks details about the return format (e.g., URL or image), but given the simplicity and annotations, it is reasonably complete.

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 coverage is 100%, so baseline is 3. The description adds meaning by explaining the role of each parameter (table_name as source, labels as category column, columns as values) and how they map to chart elements, exceeding baseline.

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, distinguishes from siblings by noting to use plot_line_chart for time-series, and mentions alternative names like spider chart. The verb 'generate' and resources are explicit.

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?

Explicitly tells when to use (user asks for spider/radar/web chart or multi-dimensional comparison) and when not to (time-series/trend data → plot_line_chart). Also includes a strong directive not to pre-fetch data using 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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