fci-mcp
Server Details
Convert handwriting into text, LaTeX math, chemistry, or molecular structures.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 4.7/5 across 6 of 6 tools scored.
Each tool has a clearly distinct purpose: layout generation, image recognition, handwriting canvas, style canvas, and two internal tools. The internal tools are explicitly marked as not to be called directly, so no ambiguity for the agent.
All tool names follow a consistent verb_noun pattern (create_layout, recognize_handwriting, recognize_image, show_handwriting_canvas, show_style_canvas, synthesize_handwriting_style). The naming is predictable and uniform.
With 6 tools, the server is well-scoped. Each tool covers a specific functionality (layout, recognition, canvas input, style capture) without unnecessary overlap or bloat.
The tool surface covers the core workflows: creating layouts, recognizing handwriting from canvas, extracting text from images, and capturing handwriting style. No obvious gaps for the stated domain of document and ink processing.
Available Tools
6 toolsfluxink-mcp-chatgpt-create-layoutARead-onlyInspect
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.
| Name | Required | Description | Default |
|---|---|---|---|
| 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
| Name | Required | Description |
|---|---|---|
| error | No | |
| title | No | |
| success | Yes | |
| layout_type | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds behavioral context beyond annotations: it mentions the PDF preview widget, the backend analysis, and the rule to not re-call if already visible. No contradiction with annotations (readOnlyHint=true is consistent with generating PDFs).
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 well-structured with clear sections and front-loaded purpose. However, it is somewhat verbose with repeated layout descriptions; a more concise version could remove redundancy.
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?
Given the tool's complexity (3 params, output schema present), the description covers all aspects: purpose, usage, parameter semantics, and behavioral rules. It is complete and leaves no critical gaps for an AI agent.
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?
With 100% schema coverage, the baseline is 3. The description adds value by emphasizing to pass full text verbatim and explaining each layout_type in detail, though some detail is redundant with the enum descriptions.
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: generating structured PDFs from raw text using seven specific templates. It distinguishes itself from sibling handwriting tools by explicitly listing layout types and use cases.
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 provides explicit when-to-use and when-not-to-use guidance, including alternatives like show_handwriting_canvas and show_style_canvas. It also gives operational rules like passing verbatim content and avoiding re-calls.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
fluxink-mcp-chatgpt-recognize-handwritingARead-onlyInspect
Internal FluxInk handwriting recognition execution endpoint. Invoked ONLY by the handwriting canvas widget when the user finishes drawing. Do NOT call this tool directly from chat. To capture handwriting from the user, call show_handwriting_canvas instead. This endpoint requires a JSON stroke payload that only the widget can produce.
| Name | Required | Description | Default |
|---|---|---|---|
| mode | No | Widget-internal recognition family. Preferred values are general or structure; legacy text, math, and chemical values are accepted for existing widget compatibility. | general |
| strokes | Yes | JSON encoded array of strokes captured by the widget. Each stroke is an object with x, y, t arrays. | |
| language | No | Optional language hint for text mode. |
Output Schema
| Name | Required | Description |
|---|---|---|
| error | No | |
| result | No | |
| success | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond annotations (readOnlyHint, destructiveHint), the description adds that the tool requires a specific JSON stroke payload from the widget and is an internal endpoint, providing useful behavioral context.
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?
Two sentences, no filler, front-loaded with critical info. Every sentence serves a purpose.
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?
Given the tool's internal nature and sibling context, the description fully covers purpose, restrictions, parameter context, and output schema existence is not required to be explained.
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?
Schema has 100% coverage with descriptions; the description adds extra meaning by explaining the strokes parameter format and possible mode values, going beyond the schema.
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 it is an internal handwriting recognition endpoint, explicitly distinguishes from sibling tools like show_handwriting_canvas, and specifies it is invoked by the widget, not directly.
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?
Provides explicit directives: 'Do NOT call this tool directly from chat' and instructs to use show_handwriting_canvas instead, giving clear when-to-use and when-not-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
fluxink-mcp-chatgpt-recognize-imageARead-onlyInspect
FluxInk image text recognition. Extract text from an EXISTING image (file upload, URL, or base64) the user provides. Higher accuracy than built in vision for tightly packed text, multi language documents, math, and LaTeX. Returns per region confidence and detection coordinates.
Use this when the user uploads, attaches, or shares an image containing text they want extracted (screenshot, photo, scanned document, receipt, sign, whiteboard, book page, photographed handwritten notes). Use this when the user wants a photographed math expression or chemistry formula converted to LaTeX. Use this when the user references an image and asks to read, transcribe, or digitize the text in it.
Do NOT use this when the user has NOT provided an image yet. Do NOT use this when the user wants to draw or handwrite something fresh (call show_handwriting_canvas instead). Do NOT use this when the request is a plain text question, summary, or explanation with no image attached.
The language parameter selects the recognition mode. en is English text (default). zh is Chinese text. paddle is multilingual or mixed text. formula is mathematics. latex returns raw LaTeX markup. After returning results, present the extracted text clearly and offer follow up actions like translation, summarization, or editing.
| Name | Required | Description | Default |
|---|---|---|---|
| image | Yes | Image to recognize text from. Pass an image from the conversation. | |
| language | No | Recognition mode. Allowed values. en (English text). zh (Chinese text). paddle (multilingual mixed text). formula (mathematics with LaTeX output). latex (return raw LaTeX markup). | en |
Output Schema
| Name | Required | Description |
|---|---|---|
| error | No | |
| results | No | |
| success | Yes | |
| language | No | |
| full_text | No | |
| total_time_ms | No | |
| detection_count | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark readOnlyHint=true. The description adds value by claiming higher accuracy for specific text types, mentioning output fields (per-region confidence, coordinates), and explaining language modes. Does not contradict annotations.
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?
Well-structured with summary, separate paragraphs for 'Use this when' and 'Do NOT use'. Slightly verbose but every sentence adds value. Front-loaded with core purpose.
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?
Covers usage, parameters, behavior, output format, and follow-up actions. Tool has output schema, so return values are already documented. Description is comprehensive for a parameter-light tool.
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?
Schema covers 100% parameters, baseline 3. Description adds meaning by explaining language parameter modes in plain language and clarifying image input formats (file upload, URL, base64). goes beyond schema.
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 extracts text from existing images, names specific sources (screenshot, photo, scanned document, etc.), and distinguishes from sibling tools like recognize-handwriting and show-handwriting-canvas. The verb 'recognize image' is effectively defined as text extraction.
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?
Explicitly lists when to use (user provides image with text, wants transcription/digitization) and when not to use (no image provided, wants handwriting/drawing), with specific sibling alternatives (show_handwriting_canvas). No ambiguity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
fluxink-mcp-chatgpt-show-handwriting-canvasARead-onlyInspect
Open the FluxInk handwriting recognition canvas. The user draws freehand strokes with a stylus, finger, or mouse. The strokes are converted by one of two model families: general recognition for handwriting, math, and chemical formulas, or structure recognition for molecular structures.
Use this when the user asks to handwrite, draw, sketch, ink, scribble, or scrawl something. Use this when the user wants to draw a math equation, chemical formula, or molecular structure rather than type it. Use this when the user asks for a canvas, drawing pad, handwriting input box, or whiteboard. Use this when the user wants to convert stylus or finger drawings into recognized text or markup.
Do NOT use this when the user types a question, equation, or formula in chat and just wants an answer. Do NOT use this when the user uploads or references an existing image of handwriting (call recognize_image instead). Do NOT use this when the user wants a formatted document, study sheet, or layout PDF (call create_layout instead). Do NOT use this when the user wants text rendered in a personal handwriting style (call show_style_canvas instead). Do NOT use this for conversational or informational requests that need no ink input.
Do NOT re-open if a FluxInk handwriting canvas is already visible from any earlier turn. Instead instruct the user to keep drawing on the existing canvas. Only set force_new=true when the user explicitly asks for a brand new, fresh, or blank canvas.
Always pass the original chat message in the prompt parameter so context is preserved after recognition. After calling, write a single short acknowledgement and do NOT describe the canvas UI.
| Name | Required | Description | Default |
|---|---|---|---|
| mode | No | Recognition family for the canvas. Use general for text, math, and chemical formulas; use structure only for molecular structures. | general |
| prompt | No | The original chat message that triggered the canvas. Passed through to the follow-up message after recognition. | |
| language | No | Optional language hint for text mode. Omit to use the mixed-language model default. | |
| force_new | No | Set true ONLY when the user explicitly asks for a brand-new canvas. Otherwise leave false so a previously opened canvas is reused. |
Output Schema
| Name | Required | Description |
|---|---|---|
| mode | Yes | |
| reuse | No | |
| prompt | Yes | |
| message | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate readOnlyHint=true, and the description adds context about the canvas opening, recognition flow, and post-call acknowledgement. It does not contradict annotations and provides additional behavioral cues beyond safety labels.
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 well-structured with a clear opening, usage blocks, and notes. It is somewhat lengthy but every sentence serves a purpose. Minor redundancy in exclusions exists, but overall efficient.
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?
Given tool complexity (multiple recognition modes, sibling tools, canvas lifecycle), the description covers when to use, alternatives, parameter behavior, and post-call actions thoroughly. Owns its completeness despite existing annotations and output schema.
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?
Schema coverage is 100%, so baseline is 3. The description adds value by clarifying the usage of 'prompt' and 'force_new' parameters beyond schema descriptions, despite repeating some enum guidance.
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 opens a handwriting recognition canvas and describes its capabilities (drawing, recognition of handwriting, math, chemical formulas, molecular structures). It explicitly distinguishes from siblings by listing what not to use it for, such as recognizing images or creating layouts.
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 provides explicit 'Use this when...' and 'Do NOT use this when...' conditions with specific alternative tool names. It also advises on when to force a new canvas and to preserve the original chat message, leaving no ambiguity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
fluxink-mcp-chatgpt-show-style-canvasARead-onlyInspect
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.
| 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
| Name | Required | Description |
|---|---|---|
| text | Yes | |
| reuse | No | |
| message | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations (readOnlyHint=true, destructiveHint=false) are complemented by descriptive behavior details: reusing existing canvas, requiring complete text, supporting English and Chinese, and prohibiting truncation. The description adds context beyond what annotations convey, such as the spritesheet output and language support.
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 thorough but somewhat lengthy; however, it is well-structured with paragraphs for purpose, usage rules, and behavioral notes. Each sentence serves a distinct informative purpose, and key points are front-loaded. Minor redundancy could be trimmed, but overall it is efficient given the tool's complexity.
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?
Given the tool's interactive nature and output (spritesheet), the description covers all necessary aspects: purpose, when to/not to use, parameter handling, language support, and UI behavior. With an output schema present, the agent can fully understand the tool's operation and output format without gaps.
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?
Both parameters ('text' and 'force_new') have comprehensive descriptions in the schema, and the description reinforces their semantics with behavioral rules (e.g., 'Always pass the COMPLETE target text', 'Only set force_new=true on explicit request'). This adds meaningful guidance beyond the schema.
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 primary function: opening a canvas to capture handwriting style and rendering text as a downloadable PNG spritesheet. It explicitly distinguishes itself from sibling tools like 'show_handwriting_canvas', 'recognize_image', and 'create_layout', leaving no ambiguity about its unique purpose.
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 provides explicit 'Use this when' and 'Do NOT use this' sections, listing clear scenarios and alternative tools for each negative case. This gives the agent precise criteria for selection, such as avoiding use for handwriting recognition or generic fonts.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
fluxink-mcp-chatgpt-synthesize-handwriting-styleARead-onlyInspect
Internal FluxInk natural texture ink execution endpoint. Invoked ONLY by the style canvas widget when the user clicks Generate. Do NOT call this tool directly from chat. To render text in the personal handwriting style of the user, call show_style_canvas instead. This endpoint requires JSON encoded style strokes captured by the widget.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Text to synthesize in the captured handwriting style. | |
| cell_size | No | Character cell size in pixels. | |
| legibility | No | Legibility level (0.0 to 2.0). | |
| style_text | No | Text the user actually wrote as the style sample (for accurate style conditioning). If omitted, the backend auto recognises via handwriting recognition. | |
| stroke_width | No | Output stroke width (1.0 to 15.0). | |
| style_strokes | Yes | JSON encoded array of style strokes captured from the canvas. |
Output Schema
| Name | Required | Description |
|---|---|---|
| error | No | |
| success | Yes | |
| charCount | No | |
| imageBase64 | No | |
| inferenceTimeMs | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is clear. The description adds that this is an internal execution endpoint requiring JSON encoded style strokes, providing adequate behavioral context beyond annotations. No contradictions.
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 extremely concise, consisting of two short sentences. The critical warning is front-loaded, and every sentence is necessary. No wasted words.
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?
Given that an output schema exists and annotations cover readonly and destructive behavior, the description provides complete context: it identifies the tool as internal, specifies when to use the alternative widget, and mentions the required input format. No gaps remain for the agent.
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?
All 6 parameters have descriptions in the input schema (100% coverage). The description does not add significant extra meaning beyond what the schema already provides, so the baseline score of 3 is appropriate.
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 as an internal endpoint for synthesizing handwriting in the user's style. It uses specific verbs ('synthesize', 'render') and distinguishes itself from the sibling tool 'show_style_canvas' by warning not to call it directly.
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 explicitly tells the agent when not to use this tool ('Do NOT call this tool directly from chat') and provides the alternative ('call show_style_canvas instead'). This is exemplary usage guidance.
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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