fci-mcp

fluxink-mcp-chatgpt-show-style-canvas

Read-only

Open the FluxInk natural texture ink style capture canvas. The user draws a few sample characters in a personal handwriting style. The widget then renders the requested text as a PNG spritesheet IN THAT EXACT PERSONAL HANDWRITING STYLE. Output is a downloadable spritesheet of individual handwritten glyphs that can be used as an asset (for example with image generation).

Use this when the user asks to render or generate text in a personal handwriting style. Use this when the user wants a personalized handwritten note, card, letter, invitation, journal entry, or signature line that should look hand written by the user. Use this when the user wants to capture, clone, or sample a personal handwriting style and reuse it.

Do NOT use this when the user just wants handwriting recognized (call show_handwriting_canvas instead). Do NOT use this for generic decorative handwritten fonts, calligraphy art, or AI generated script unrelated to the personal writing of the user. Do NOT use this to read text from an existing photo of handwriting (call recognize_image instead). Do NOT use this when the user wants a formatted document or layout (call create_layout instead). Do NOT use this for plain informational requests.

Supports English and Chinese. Always pass the COMPLETE target text in the text parameter. Never truncate or abbreviate. Every character must appear in the spritesheet.

Do NOT re-open if a FluxInk natural texture ink canvas is already visible from any earlier turn. Instead instruct the user to update the target text inside the existing widget. Only set force_new=true on an explicit user request for a brand new style canvas.

After calling, write a single short acknowledgement and do NOT describe the UI. Once the user saves the spritesheet they can re-upload it for further design work.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`text`	Yes	The COMPLETE text to render as natural texture ink handwriting. Pass the FULL text. Never truncate or abbreviate. Supports English and Chinese.
`force_new`	No	Set true ONLY when the user explicitly asks for a brand-new style canvas. Otherwise leave false so a previously opened canvas is reused.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`text`	Yes
`reuse`	No
`message`	Yes

TDQS

A4.6/5.0

Behavior4/5

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

Annotations indicate readOnlyHint=true and destructiveHint=false, so the description adds value by detailing the canvas interaction, language support, and the critical 'do not re-open if already visible' rule. No contradiction with 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?

The description is well-organized into sections, though somewhat lengthy. Every sentence adds necessary information with no redundancy. Minor improvements could shorten examples, but current structure is effective.

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?

With an output schema present, the description covers all necessary aspects: purpose, usage conditions, parameter constraints, and behavioral notes (e.g., do not re-open). It also notes language support. Fully sufficient for correct tool invocation.

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 covers 100% of parameters, but the description reinforces the 'complete text' requirement and explains when to set force_new=true. This adds pragmatic context beyond schema descriptions.

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 the tool opens a canvas for capturing personal handwriting and renders text as a PNG spritesheet in that style. It distinguishes from siblings by explicitly naming alternatives and use cases, making the purpose unambiguous.

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?

Provides explicit when-to-use (personal handwriting style, handwritten notes) and when-not-to-use lists with specific alternative tools (show_handwriting_canvas, recognize_image, create_layout). This leaves no ambiguity for the AI agent.

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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TDQS

A4.5/5.0

Disambiguation5/5

Each tool has a clearly distinct purpose: layout generation, handwriting canvas, style canvas, image text recognition, and two internal endpoints. The descriptions are highly detailed with explicit 'Do NOT use' instructions, making it easy for an agent to select correctly.

Naming Consistency4/5

Tool names follow a consistent verb-noun pattern (create, recognize, show, synthesize) with hyphens. Minor inconsistency: two 'recognize' tools could cause confusion, but one is clearly marked as internal. Overall, the naming is predictable and readable.

Tool Count5/5

With 6 tools, the server is well-scoped for its domain of document layout, handwriting input, OCR, and style generation. Each tool earns its place, and the count is neither too small nor excessive.

Completeness4/5

The tool set covers the main workflows: layout creation, handwriting capture/recognition, image OCR, and personal handwriting style synthesis. Minor gaps like lack of an edit tool for layouts are understandable given the domain; agents can work around them.

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