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generate_chart

Render a chart to a hosted image; returns a URL to embed in markdown as title. Types: bar (grouped vertical), horizontal_bar (best for long category names or ranked lists), stacked_bar (part-to-whole across categories), line / area (trend over an ordered axis), pie / doughnut (share-of-total of ONE series).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYesPick the type that best fits the data.
titleYes
labelsYesCategory labels (x axis; slice labels for pie/doughnut).
seriesYesOne or more series; each values array aligns with labels. pie/doughnut use the first series only.
valueSuffixNoe.g. '%' or null.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Annotations declare readOnlyHint=false, destructiveHint=false, and openWorldHint=false, but the description usefully adds what the tool actually produces: a hosted image URL formatted for markdown embedding. That return-format disclosure is genuine value beyond the annotations.

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?

Front-loaded with the core action and return value, then a compact type-to-purpose map. It is dense but every clause carries selection-relevant information; slightly list-heavy but no wasted sentences.

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?

With an output schema present, the description needn't detail return shape, and it covers purpose, output URL, and type selection well. It leaves only ancillary questions (limits, styling) unanswered, which is reasonable for this tool.

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

Parameters4/5

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

Schema coverage is 80%, but the description meaningfully enriches the most consequential parameter (type) far beyond the schema's terse 'Pick the type that best fits the data,' noting horizontal_bar for long category names and that pie/doughnut take a single series. It adds real meaning on top of the schema.

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?

States a specific verb and resource ('Render a chart to a hosted image') plus the concrete output ('returns a URL to embed in markdown as ![title](url)'). This clearly distinguishes it from every sibling, none of which produce charts or hosted images.

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

Usage Guidelines3/5

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

The description gives strong guidance for choosing among chart types, but that is really selection within the enum. It never states when to reach for this tool versus its siblings or any preconditions, so usage vs alternatives is only implied.

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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