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Teradata

Teradata MCP Server

Official
by Teradata

plot_line_chart

Generate a line chart from a Teradata table using a label column for x-axis and one or more columns for y-axis. Specify table name, label, and data columns.

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 annotations are provided, so the description must fully disclose behavior. It only states it generates a plot and returns a dict, but fails to mention side effects (e.g., read-only), error conditions, or output structure beyond 'dict'. The copy-paste error further undermines reliability.

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 explicit PARAMETERS and RETURNS sections, making it easy to parse. However, it is not maximally concise due to the copy-paste error and repetitive phrasing (e.g., 'Required Argument' for each parameter).

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?

Given no output schema and no annotations, the description should cover prerequisites (e.g., numeric columns for y-axis), required permissions, and error handling. It does not, leaving significant gaps for an agent to safely invoke the tool.

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?

The description adds meaning by specifying that labels are for the x-axis and columns for the y-axis, which goes beyond the schema's basic type and requirement info. However, the schema description for 'table_name' contains an erroneous reference to 'donut plot', and parameter descriptions are largely redundant with the schema, preventing a higher score.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

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 explains that labels are for x-axis and columns for y-axis, distinguishing it from sibling chart tools. However, a copy-paste error mentions 'donut plot' in the parameter descriptions, slightly reducing clarity.

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 explicit guidance on when to use this tool over alternatives or any prerequisites. The description lacks context on data suitability or required conditions, leaving the agent to infer usage from the tool name alone.

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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