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Teradata

Teradata MCP Server

Official
by Teradata

plot_pie_chart

Read-onlyIdempotent

Create a pie chart directly from a Teradata table to visualize proportions. Specify the table, category column for labels, and numeric value column.

Instructions

Generate a pie chart that reads directly from a Teradata table — do NOT use base_readQuery to pre-fetch or aggregate data first. Specify the table in table_name, the category column in labels, and the numeric value column in column. Use when the user asks for proportions, shares, or how a total breaks down by category. For polar area charts, use plot_polar_chart. For time-series trends, use plot_line_chart.

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

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

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

RETURNS: dict

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
columnYes Required Argument. Specifies the numeric value column for the pie 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 pie chart. Types: str
Behavior4/5

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

Given annotations already indicate readOnlyHint and idempotentHint, the description adds valuable context: it reads directly from the table without pre-fetching, and instructs not to use base_readQuery. This goes beyond mere restatement.

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 concise with 4 sentences, front-loading the purpose. It uses backticks for parameters and includes usage guidance. It is slightly longer than necessary but still efficient.

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 3-parameter tool with rich annotations, the description fully covers purpose, usage, parameters, and alternatives. It leaves little ambiguity for the agent, making it highly complete.

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 the schema descriptions already specify each parameter's role. The main description integrates these roles but adds no new semantic information beyond what the schema provides. Baseline score of 3 applies.

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 'Generate a pie chart that reads directly from a Teradata table' with specific verb and resource. It distinguishes from siblings by explicitly referencing plot_polar_chart and plot_line_chart for alternative purposes.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

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

The description tells when to use: 'when the user asks for proportions, shares, or how a total breaks down by category'. It provides explicit alternatives for polar and line charts, and warns against using base_readQuery. However, it does not exhaustively cover all sibling tools.

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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