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Create Chart Image

chart.visualization.create
Idempotent

Generate a permanent image URL of a data chart (bar, line, pie, doughnut, radar, scatter) by providing chart type, labels, and datasets. Ideal for visualizing any dataset when combined with data tools.

Instructions

⚡ ACTION: Generate a chart image (PNG) from data — bar, line, pie, doughnut, radar, scatter. Returns a permanent image URL. Combine with data tools (climate.co2, census.population, finance.exchange_rates) to visualize any dataset. Powered by QuickChart (Chart.js).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYesChart type: bar, line, pie, doughnut, radar, scatter, horizontalBar
labelsYesX-axis labels (e.g. ["Q1", "Q2", "Q3", "Q4"])
datasetsYesOne or more datasets to plot
titleNoChart title displayed at top
widthNoImage width in pixels (100-1000, default 500)
heightNoImage height in pixels (100-1000, default 300)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultNoTool response payload. Shape varies per tool — consult the tool description and inputSchema. May be an object, array, string, or number depending on the upstream provider response.
errorNoPresent only when the call failed. Includes error code, message, request_id, and any provider-specific extras.
Behavior3/5

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

Annotations already declare idempotentHint=true and openWorldHint=true, so the description adds little behavioral context. It notes the output is a permanent image URL and the technology backend (QuickChart/Chart.js), but does not detail side effects or constraints 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.

Conciseness5/5

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

Two sentences with no fluff. The first sentence delivers core purpose and supported types; the second provides usage guidance and technical context. Highly 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, output format, and suggested usage. The schema and annotations cover the rest. Could mention default dimensions or error cases, but overall sufficient for a well-documented tool.

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 the schema fully documents each parameter. The description adds no additional semantic information beyond what is in the schema, resulting in a baseline score.

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?

Clearly states it generates a chart image (PNG) from data, lists supported chart types (bar, line, pie, etc.), and mentions it returns a permanent URL. No sibling tools for chart creation, so differentiation is inherent but well-stated.

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

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit context on when to use (visualizing data from other tools) and suggests combining with specific data tools. However, no explicit when-not or alternative tools are mentioned, though no direct alternatives exist.

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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