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Render an existing scene

render_scene
Read-onlyIdempotent

After editing scene elements, render the scene to display the updated diagram. Use this as the final step to show changes.

Instructions

Render a stored scene and show it to the user.

USE THIS after a batch of add_element / update_element / remove_element / group_elements calls, to display the updated diagram. It is the last step of every edit.

This never changes the scene - it only draws what is currently in it. For a brand new diagram use render_diagram instead, which builds and shows it in one call.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sceneIdYesId of the scene to work on, as returned by render_diagram or create_scene.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
svgNoThe rendered SVG markup.
errorNoPresent only when success is false.
titleNo
widthNo
formatNo
heightNo
svgUrlNoStable link to the rendered SVG, when deployed.
sceneIdNo
successYes
elementCountNo
Behavior4/5

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

Annotations already provide readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds value by explaining the tool 'never changes the scene - it only draws what is currently in it', which reinforces the behavioral contract beyond the annotations. A slight deduction is that it doesn't mention any potential rendering delays or failure modes.

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 with only three sentences, all front-loaded with the core purpose. Every sentence adds value: purpose, usage guidelines, and exclusion of alternatives. No wasted words.

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

Completeness5/5

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

Given the tool has 1 parameter with full schema coverage, an output schema, and comprehensive annotations, the description is complete. It provides all necessary context for an agent to select and invoke this tool correctly: purpose, usage flow, behavioral safety, parameter source, and sibling differentiation.

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 the baseline is 3. The description adds some value by mentioning the sceneId is 'as returned by render_diagram or create_scene', providing context on how to obtain valid IDs. However, it doesn't add new meaning about the parameter format beyond the schema's pattern.

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 states this tool renders a stored scene and shows it to the user. It uses specific verbs ('render', 'show', 'display') and identifies the resource ('stored scene', 'updated diagram'), distinguishing it from siblings like render_diagram which builds a new diagram.

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

Usage Guidelines5/5

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

The description explicitly tells when to use this tool: after a batch of editing calls (add_element, update_element, etc.) as the last step of every edit. It also clearly states when NOT to use it: for brand new diagrams, use render_diagram instead. This provides excellent context for an AI agent to decide.

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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