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manzoor-source

Teradata MCP Server

plot_line_chart

Read-onlyIdempotent

Generate a line chart directly from a Teradata table using a date/time column for x-axis and numeric columns for y-axis. Ideal for visualizing time-series trends and sequential data.

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=true and idempotentHint=true, so the description's statement of reading directly from a table aligns and adds no contradiction. It adds a behavioral constraint (do not use base_readQuery) which provides additional guidance 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?

Well-structured with clear purpose, usage notes, and parameter descriptions. Each sentence adds value. Slightly verbose with repeated parameter descriptions across description and schema, but overall efficient.

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?

Covers purpose, usage guidelines, parameter semantics, and returns. Since there is no output schema, the return type (dict) is mentioned. Could be more specific about return format, but sufficient for an agent to invoke correctly.

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%, so baseline is 3. The description adds context by specifying that labels is typically date/time and columns are numeric, but much of the parameter info is repeated from the schema.

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 chart from a Teradata table, specifies the source type (table), and explicitly distinguishes from siblings (plot_pie_chart, plot_polar_chart, plot_radar_chart) and from base_readQuery.

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 usage context: time-series, trend lines, sequential data. Includes clear 'do not use' conditions: proportional breakdowns (use pie/polar) and multi-dimensional spider comparisons (use radar). Also instructs not to pre-fetch with base_readQuery.

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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