fci-mcp

fluxink-mcp-chatgpt-create-layout

Read-only

FluxInk document layout generator. Transform raw text content into a structured PDF using one of seven study or work templates, then preview it in an embedded PDF viewer widget.

Supported layout_type values. cornell is the Cornell note taking layout with cue, notes, and summary. bullet_points is a clean bulleted summary. zettelkasten is atomic linked notes. journalism_5w1h is who, what, when, where, why, and how. meeting_add is a meeting agenda plus action items. sq3r is Survey, Question, Read, Recite, Review study notes. pso is Problem, Solution, Outcome.

Use this when the user asks for a Cornell sheet, bulleted summary, Zettelkasten card, 5W1H breakdown, meeting agenda or minutes, SQ3R study sheet, or PSO writeup. Use this when the user wants to turn raw notes, lecture transcript, or source material into a printable PDF or formatted study sheet. Use this when the user asks for a downloadable PDF document of their content.

Do NOT use this when the user just asks for a plain summary in chat. Give it inline. Do NOT use this when the user wants to handwrite or draw something. Call show_handwriting_canvas instead. Do NOT use this when the user wants text in a personal handwriting style. Call show_style_canvas instead. Do NOT use this for plain informational requests with no document generation intent.

Always pass the source material verbatim in the content parameter. Do NOT pre summarize. The layout engine handles structuring. Pick the layout_type that best matches the stated purpose. If unclear, ask one short clarifying question instead of guessing.

Do NOT re-call if a layout PDF is already visible from a previous turn unless the user explicitly asks for a different layout, different content, or a regeneration. After calling, write a single short acknowledgement and do NOT restate the PDF content.

Input Schema

TableJSON Schema

Name	Required	Description
`title`	No	Optional title for the document. If omitted, the backend generates one.
`content`	Yes	The raw educational or note content to transform into a structured layout. Pass the full text. The backend will analyze, structure, and render it.
`layout_type`	Yes	Layout template to render. Allowed values. cornell (Cornell note taking layout with cue, notes, and summary). bullet_points (clean bulleted summary). zettelkasten (atomic linked notes). journalism_5w1h (who, what, when, where, why, how). meeting_add (meeting agenda plus action items). sq3r (Survey, Question, Read, Recite, Review study notes). pso (Problem, Solution, Outcome).

Output Schema

TableJSON Schema

Name	Required	Description	Default
`error`	No
`title`	No
`success`	Yes
`layout_type`	No

TDQS

A4.9/5.0

Behavior5/5

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

Discloses important behaviors beyond annotations: re-call policy, post-call acknowledgement requirement, and instruction to pass content verbatim without pre-summarizing. No contradictions 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?

Well-structured with a clear summary, template list, and usage rules. Slightly lengthy but every sentence adds value.

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?

Covers all aspects: purpose, parameters, when to use, when not to use, post-call behavior, and content handling. Rich and complete for a 3-parameter tool with output schema and annotations.

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?

All three parameters have schema descriptions (100% coverage). Description adds extra context, especially for layout_type with detailed enumeration and content handling advice.

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?

Clearly states it is a document layout generator that transforms raw text into structured PDFs using seven templates. Distinguishes from sibling tools by specifying when not to use.

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?

Explicitly lists use cases (e.g., Cornell sheet, bulleted summary) and exclusions (plain chat summary, handwriting). Provides alternative tools and instructions for content handling.

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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