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Scatter Plot

plot_scatter
Read-onlyIdempotent

Create a scatter plot to visualize the relationship between two numerical datasets, with customizable title, labels, color, and point size.

Instructions

Create a scatter plot from data points (requires matplotlib).

Examples: plot_scatter([1, 2, 3, 4], [1, 4, 9, 16], title="Correlation Study") plot_scatter([1, 2, 3], [2, 4, 5], color='purple', point_size=100)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
colorNoPoint color (name or hex code, e.g., 'blue', '#2E86AB')
titleNoChart title string, e.g., 'Correlation Study'Scatter Plot
x_dataYesX-axis data points, e.g., [1, 2, 3, 4]
y_dataYesY-axis data points, e.g., [1, 4, 9, 16]
x_labelNoX-axis label, e.g., 'Variable X'X
y_labelNoY-axis label, e.g., 'Variable Y'Y
point_sizeNoScatter point size in points^2, e.g., 50
Behavior3/5

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

Annotations already declare readOnlyHint and idempotentHint, covering safety. The description adds a dependency requirement (matplotlib) but no other behavioral traits. With annotations, the bar is lower; this is adequate but minimal.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise: two short sentences and two examples. It is front-loaded and every sentence serves a purpose. No wasted 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?

With 7 parameters and no output schema, the description lacks explanation of what happens after plotting (e.g., display or return). While annotations cover safety, the brief description does not fully address the tool's runtime behavior.

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 description coverage is 100%, so baseline is 3. The description adds no extra parameter details beyond examples that mirror the schema. No additional meaning provided.

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 specifies 'Create a scatter plot from data points', which is a clear verb-resource pair. However, it does not distinguish from sibling plot tools like plot_line_chart or plot_histogram, which have similar formats.

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 does not provide any guidance on when to use a scatter plot versus alternatives. It only notes a dependency (matplotlib) but no context for selection among siblings.

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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