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ceeyang-ai

Viz MCP Server

by ceeyang-ai

create_scatter_plot

Create a scatter plot to visualize the relationship between two numeric variables. Optionally add point labels, a trend line, and customize colors.

Instructions

Create a scatter plot.

Args: x_values: X data points y_values: Y data points labels: Optional point labels (for hover) title: Chart title xlabel: X-axis label ylabel: Y-axis label palette: Color palette: default, vibrant, pastel, monochrome regression_line: Show trend line output: 'svg' for inline SVG, 'png' for file path

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
x_valuesYes
y_valuesYes
labelsNo
titleNoScatter Plot
xlabelNo
ylabelNo
paletteNodefault
regression_lineNo
outputNosvg

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

With no annotations, the description should disclose behavioral traits. It mentions output options (svg/png) but fails to address side effects, permissions, data validation, or processing behavior. The core creation action is stated, but additional important behavioral details are missing.

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?

The description is structured as a clean parameter list with concise explanations. While slightly lengthy due to nine parameters, it is front-loaded with the purpose and efficiently uses bullet-like syntax. No unnecessary words.

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

Completeness3/5

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

Given the presence of an output schema (not shown), the description need not detail return values. However, it lacks information on requirements, error conditions, or data constraints. For a complex tool with many options, it feels slightly incomplete, but covers the basics adequately.

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

Parameters5/5

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

The schema has 0% description coverage, so the description provides critical meaning for each parameter (e.g., 'X data points', 'Optional point labels (for hover)'). It also documents allowed values for 'palette' and 'output', which are absent from the schema. This fully compensates for the schema gap.

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 'Create a scatter plot,' which is a specific verb and resource. The tool name and description immediately distinguish it from sibling tools like create_bar_chart, create_histogram, etc., making the purpose unambiguous.

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

Usage Guidelines1/5

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 such as create_line_chart or create_histogram. There is no mention of prerequisites, context, or exclusions, leaving the agent to infer usage without support.

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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