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manzoor-source

Teradata MCP Server

plot_pie_chart

Read-onlyIdempotent

Generate a pie chart directly from a Teradata table to visualize proportions or share of a numeric column by category.

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?

Annotations indicate readOnlyHint and idempotentHint are true. The description adds important context that the tool reads directly from Teradata without pre-fetching, reinforcing safety. However, it could briefly mention that no data modification occurs.

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 reasonably concise but repeats parameter details that are also in the schema. It could be slightly tighter, but the structure is effective with purpose, usage, and parameter explanation in one paragraph.

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?

The description covers the essential aspects for a simple 3-parameter tool: purpose, usage, and return type. It lacks details about the returned dict structure, but given no output schema and low complexity, this is acceptable.

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%, but the description adds meaning beyond the schema by explaining the role of each parameter (e.g., 'category column for labels', 'numeric value column'). This helps the agent understand how to map user requests to parameters.

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 that the tool generates a pie chart from a Teradata table, specifying the table name, labels column, and value column. It distinguishes itself from sibling tools like plot_polar_chart and plot_line_chart by explicitly naming them as alternatives for polar area charts and time-series trends.

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 explicitly states when to use the tool (for proportions, shares, breakdowns) and what not to do (do not pre-fetch data with base_readQuery). It also provides clear alternatives for related chart types, aiding correct tool selection.

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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