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graphgrow
by graphgrow

viz_recipes

Read-only

Obtain chart templates for engineering plots: line, scatter, bar, histogram, cdf, error bars, heatmaps, Bode, FFT, and more. Each template provides a ready-to-adapt specification for your data.

Instructions

Engineering chart recipes — call with no topic for the index, with a topic for a worked template: line, scatter, bar, histogram, cdf, log_axes, error_bars, tolerance_band, control_chart, heatmap (Vega-Lite, chart_spec-ready) and bode, fft, contour (python, via py_run). Templates teach the shape; adapt fields to your data.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topicNo
Behavior4/5

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

Annotations already declare readOnlyHint=true, so no need to restate safety. The description adds behavioral context by explaining that the tool returns a "worked template" ("Templates teach the shape; adapt fields to your data") and that the output varies by topic (Vega-Lite vs Python), going 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.

Conciseness5/5

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

The description is two sentences, information-dense, and free of fluff. The first sentence front-loads action instructions and topic lists, while the second sentence adds a concise summary of the template philosophy, all without unnecessary words.

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 single-optional-parameter tool, the description is sufficient to use it correctly: it explains the two call modes, lists topics, and indicates output types. It doesn't specify the exact structure of the index or template, but this is a minor gap given the absence of an output schema and the tool's simplicity.

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?

Schema coverage is 0%, so the description fully compensates by enumerating all valid topic values and describing the effect of empty vs non-empty topic. It tells the agent that no topic yields an index and a topic yields a worked template, with explicit topic names and output types, making the parameter's semantics complete.

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 indicates that viz_recipes provides chart templates ("Engineering chart recipes") and explains the two modes of calling: with no topic for an index, or with a topic for a specific worked template. It lists the available topics and their output formats (Vega-Lite vs Python), distinguishing it from sibling tools like chart_spec and py_run.

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?

The description gives explicit instructions on when to call with or without a topic, and notes the integration with chart_spec and py_run by stating output formats ("Vega-Lite, chart_spec-ready" and "python, via py_run"). However, it doesn't explicitly say when not to use this tool, only implies the alternative contexts through integration points.

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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