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Teradata

Teradata MCP Server

Official
by Teradata

plot_line_chart

Generate a line plot from a Teradata table by specifying labels for the x-axis and columns for the y-axis.

Instructions

Function to generate a line plot for labels and columns. Columns mentioned in labels are used for x-axis and columns are used for y-axis.

PARAMETERS: table_name: Required Argument. Specifies the name of the table to generate the donut plot. Types: str

labels:
    Required Argument.
    Specifies the labels to be used for the line plot.
    Types: str

columns:
    Required Argument.
    Specifies the column to be used for generating the line plot.
    Types: List[str]

RETURNS: dict

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
table_nameYes Required Argument. Specifies the name of the table to generate the donut plot. Types: str
labelsYes Required Argument. Specifies the labels to be used for the line plot. Types: str
columnsYes Required Argument. Specifies the column to be used for generating the line plot. Types: List[str]
Behavior2/5

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

No behavioral traits are disclosed beyond the axis mapping. The description does not state whether the tool is read-only, has side effects, or requires specific permissions. With no annotations, the description fails to adequately convey behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is structured with sections but is somewhat verbose, repeating schema information. It could be more concise without losing clarity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description lacks information on return value format, error scenarios, and prerequisites like valid table names. Given no output schema and moderate complexity, the description leaves gaps.

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?

Although schema coverage is 100%, the description adds value by clarifying that 'labels' maps to x-axis and 'columns' to y-axis, which is not explicit in the schema descriptions alone. This helps an AI agent understand parameter roles.

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 line plot and specifies that labels are used for x-axis and columns for y-axis. This distinguishes it from sibling plot tools like pie, polar, and radar charts.

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

Usage Guidelines2/5

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

No guidance is provided on when to use this tool versus alternatives (e.g., pie chart, radar chart). There is no mention of appropriate contexts or limitations.

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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