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ceeyang-ai

Viz MCP Server

by ceeyang-ai

create_bar_chart

Generate bar charts from numeric data with customizable labels, colors, and orientation. Output as SVG inline or PNG file.

Instructions

Create a bar chart.

Args: values: Numeric values for each bar labels: Category labels (optional, auto-generated if omitted) title: Chart title xlabel: X-axis label ylabel: Y-axis label palette: Color palette: default, vibrant, pastel, monochrome horizontal: If True, render horizontal bars output: 'svg' for inline SVG string, 'png' for file path

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
valuesYes
labelsNo
titleNoBar Chart
xlabelNo
ylabelNo
paletteNodefault
horizontalNo
outputNosvg

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

With no annotations, the description must fully disclose behavior. It explains output formats (svg vs. png) and horizontal option, but does not detail file saving behavior, error handling, or data validation. Some transparency exists, but gaps remain about side effects and constraints.

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 clear parameter list, front-loading the purpose. However, it is somewhat verbose with 'Args:' formatting; each sentence earns its place but could be more succinct.

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?

For a chart creation tool, the description covers all parameters and output options, making it fairly complete. It does not mention error behavior or performance, but given the tool's simplicity and explicit parameters, it is adequate.

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 input schema has 0% description coverage, so the description fully compensates. It explains each parameter's purpose, defaults, and options (e.g., palette choices, output formats). This adds essential meaning beyond the bare schema.

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 'Create a bar chart.' and lists parameters, indicating the specific resource and action. However, it does not differentiate from sibling tools like create_histogram or create_line_chart, missing an opportunity to clarify when to use this tool over alternatives.

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 guidance is provided on when to use this tool versus sibling tools (e.g., bar chart vs. histogram). The description lacks context about preferred use cases, prerequisites, or scenarios where this tool is unsuitable.

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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