Create data visualization

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

Given annotations only indicate non-read-only and non-destructive, the description adds substantial behavioral context: it details the node creation process, the placeholder data seeding, the return payload (summary, JSON block with paths, errors, warnings, preview image), and behavior differences among chart_style values. This goes well beyond what annotations provide.

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

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

The description is four sentences that efficiently cover the tool's behavior, creation steps, wiring instruction, and return value. It is front-loaded with the primary action and avoids unnecessary filler, earning a high score.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description compensates by fully describing the return value (summary, JSON block with paths, errors, warnings, preview image). It also covers the full build process, the need to wire real data, and potential warnings. For a tool with this complexity, the context is rich and complete.

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

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

The input schema already has 100% parameter description coverage, with detailed explanations for each enum and parameter. The tool description adds little new parameter-specific meaning—only the 'Scale level' mention aligns with expose_controls. Baseline 3 is appropriate since the schema already carries the descriptive weight.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: it builds a data-driven visualization with a specific node chain (data source to CHOP to chart TOP). It lists the exact components created (baseCOMP, 'data' source, conversions, Scale, chart, Null) and names the output structure, making it distinct from sibling creation tools.

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

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

The description implies when to use (when you need a data-driven visualization chart) and includes a critical workflow instruction ('Wire your real data into the created 'data' node'). However, it doesn't explicitly contrast with alternatives like create_waveform or create_histogram_scope, so the guidance is clear but not fully exclusions-focused.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.