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LouisChanCLY

napkin-ai-mcp

by LouisChanCLY

Generate Visual

generate_visual

Create infographics, diagrams, and charts from text via Napkin AI. Returns a request ID for tracking generation progress.

Instructions

Submit a visual generation request to Napkin AI. Returns a request ID for tracking. Use check_status to poll for completion.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
widthNoWidth in pixels (PNG only)
formatNoOutput format: svg, png, or ppt (default: svg)
heightNoHeight in pixels (PNG only)
contentYesMain text content to visualise
contextNoAdditional context for visual generation
dry_runNoValidate inputs without calling the API (default: false)
languageNoBCP 47 language tag (e.g., en, en-GB). Default: en
style_idNoStyle identifier from Napkin AI
visual_idNoRegenerate a specific visual layout with new content. Cannot be used with visual_ids, visual_query, or visual_queries.
color_modeNoColour mode: light, dark, or both
visual_idsNoArray of visual IDs to regenerate specific layouts. Length must match number_of_visuals.
orientationNoOrientation: auto, horizontal, vertical, or square
visual_queryNoVisual type query (e.g., mindmap, flowchart, timeline)
sort_strategyNoSort strategy: relevance, random, or variation
visual_queriesNoArray of visual type queries. Length must match number_of_visuals.
number_of_visualsNoNumber of variations to generate (1-4)
text_extraction_modeNoText extraction: auto, rewrite, or preserve
transparent_backgroundNoUse transparent background

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesRequest ID for tracking
statusYesInitial status (usually 'pending')
warningNoAny warnings from the API
Behavior2/5

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

No annotations are provided, so the description must carry the burden. It only discloses that a request ID is returned, leaving out important behavioral details such as authentication requirements, rate limits, or whether execution is synchronous or asynchronous. Even the async nature is only implied through the polling reference.

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, front-loaded with the primary action, and no filler. Every word earns its place.

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

Completeness2/5

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

Despite having an output schema, the description is minimal for an async tool with 18 parameters and several sibling tools. It fails to mention prerequisites (e.g., API key), processing expectations, or how it differs from generate_and_wait beyond returning a request ID. The description does not fully map the tool's role within the broader toolset.

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 schema already documents all 18 parameters thoroughly. The description adds no parameter-specific meaning, and the baseline of 3 is appropriate since the heavy lifting is done by 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?

The description clearly states a specific action ('Submit a visual generation request to Napkin AI') and resource. It distinguishes from sibling tools by noting it returns a request ID and pointing to check_status for polling, unlike generate_and_wait or generate_and_save.

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?

It provides a clear workflow (submit then poll with check_status) but does not explicitly mention when to prefer this tool over alternatives like generate_and_wait, nor does it state any exclusions. The guidance is more implied than explicit.

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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