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Teradata MCP Server

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by Teradata

plot_polar_chart

Generate a polar area plot to visualize data relationships between specified labels and columns from Teradata tables for analytical insights.

Instructions

Function to generate a polar area plot for labels and columns. Columns mentioned in labels are used as labels and column is used to plot.

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

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

RETURNS: dict

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
table_nameYes
labelsYes
columnYes

Implementation Reference

  • The core handler function for the 'plot_polar_chart' MCP tool. It validates inputs, queries the specified Teradata table using the provided labels and column, and delegates to get_plot_json_data to generate JSON data formatted for a polar area chart (using Chart.js polarArea type). This is the direct implementation of the tool logic.
    def handle_plot_polar_chart(conn: TeradataConnection, table_name: str, labels: str, column: str):
        """
        Function to generate a polar area plot for labels and columns.
        Columns mentioned in labels are used as labels and column is used to plot.
    
        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
    
            column:
                Required Argument.
                Specifies the column to be used for generating the line plot.
                Types: str
    
        RETURNS:
            dict
        """
        # Labels must be always a string which represents a column.
        if not isinstance(labels, str):
            raise ValueError("labels must be a string representing the column name for x-axis.")
    
        return get_plot_json_data(conn, table_name, labels, column, 'polar')
Behavior2/5

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

No annotations are provided, so the description carries the full burden. It states the tool 'generate[s] a polar area plot' which implies a read-only visualization operation, but doesn't disclose any behavioral traits such as whether it modifies data, requires specific permissions, has rate limits, or what the output format entails (beyond 'dict'). For a tool with no annotations, this leaves significant gaps in understanding its behavior.

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 clear sections (function, parameters, returns), but it's somewhat redundant and could be more concise. The first sentence is front-loaded with the purpose, but the parameters section repeats obvious information (e.g., 'Required Argument' and 'Types: str' for all parameters) without adding value. It's appropriately sized but includes wasted verbiage.

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 the complexity (a plotting tool with 3 parameters), no annotations, no output schema, and 0% schema coverage, the description is incomplete. It lacks details on what the polar area plot represents (e.g., is it for categorical data, numerical values?), how parameters relate (e.g., does 'labels' correspond to categories and 'column' to values?), and what the return dict contains. This makes it inadequate for an AI agent to use the tool effectively without guesswork.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the schema provides no parameter descriptions. The description includes a PARAMETERS section that lists each parameter with basic types and requirements, but it adds minimal semantic meaning. For example, it states 'labels' are 'used as labels' and 'column' is 'used to plot', which is tautological and doesn't explain what data they should contain or how they interact. This partially compensates for the schema gap but inadequately.

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

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the tool 'generate[s] a polar area plot for labels and columns' which clarifies the verb (generate) and resource (polar area plot), but it's vague about what the plot actually represents. It mentions 'labels and columns' but doesn't explain the relationship between them or what the polar chart visualizes. The description distinguishes from some siblings like 'plot_line_chart' by specifying the chart type, but doesn't clearly differentiate from 'plot_radar_chart' which might be similar.

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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when a polar area plot is appropriate compared to other chart types like pie or radar charts (which are siblings), nor does it specify prerequisites or exclusions. The only implied usage is for generating this specific chart type, but without context for 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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