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Teradata MCP Server

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by Teradata

plot_line_chart

Read-onlyIdempotent

Generate a line chart directly from a Teradata table by specifying the table, x-axis column (typically date/time), and one or more y-axis numeric columns. Ideal for time-series, trend lines, or sequential data without pre-fetching.

Instructions

Generate a line 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 x-axis column in labels (typically a date or time field), and one or more y-axis numeric columns in columns. Use for time-series, trend lines, or sequential data. Do NOT use for proportional category breakdowns — use plot_pie_chart or plot_polar_chart. Do NOT use for multi-dimensional spider comparisons — use plot_radar_chart.

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

labels:
    Required Argument.
    Specifies the x-axis column (typically date or time).
    Types: str

columns:
    Required Argument.
    Specifies the y-axis numeric column(s) for the line chart.
    Types: List[str]

RETURNS: dict

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
labelsYes Required Argument. Specifies the x-axis column (typically date or time). Types: str
columnsYes Required Argument. Specifies the y-axis numeric column(s) for the line chart. Types: List[str]
table_nameYes Required Argument. Specifies the name of the table to generate the line chart. Types: str
Behavior4/5

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

Annotations already declare readOnlyHint and idempotentHint, so the safety profile is covered. Description adds that it reads directly from a database table, which is consistent and provides context about data source. No contradictions. Score reflects additional context beyond annotations.

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?

Description is fairly concise with front-loaded key behavior. The included parameter section repeats schema information, which is slightly redundant but not excessive. Clear structure with usage instructions first.

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?

Given no output schema, description adequately explains what the tool does, what it reads, and when to use it. Missing return format details, but for a charting tool, it's sufficient. Completeness for a simple tool with good annotations.

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. Description adds context that labels is 'typically a date or time field' and columns are 'numeric', which adds meaning beyond the schema's generic descriptions. Also clarifies table_name as the table to generate chart from.

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?

Description clearly states it generates a line chart from a Teradata table, with explicit verb 'generate' and resource 'line chart'. It distinguishes from siblings by specifying not to use for proportional breakdowns (use plot_pie_chart or plot_polar_chart) or spider comparisons (use plot_radar_chart).

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?

Provides explicit when-to-use (time-series, trend lines, sequential data) and when-not-to-use (proportional categories, spider comparisons) with alternatives. Also instructs not to use base_readQuery to pre-fetch data, offering a clear directive.

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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