fci-mcp

fluxink-mcp-chatgpt-recognize-image

Read-only

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.

Input Schema

TableJSON Schema

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

TableJSON Schema

Name	Required	Description	Default
`error`	No
`results`	No
`success`	Yes
`language`	No
`full_text`	No
`total_time_ms`	No
`detection_count`	No

TDQS

A4.6/5.0

Behavior4/5

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

Annotations already provide readOnlyHint=true and destructiveHint=false. Description adds context about returning per-region confidence and coordinates, and suggests follow-up actions like translation. No contradiction.

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 clear sections: purpose, usage, language parameters, and post-processing. Slightly verbose but each sentence is informative and earns its place.

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 necessary aspects: what the tool does, when/when not to use, parameter details with enum values, and guidance on what to do after returning results. No gaps given the tool's complexity.

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 coverage is 100%, so baseline is 3. Description adds value by explaining each language mode (en, zh, paddle, formula, latex) and their use cases, going 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?

Clearly states 'Extract text from an EXISTING image' with specific verb and resource. Distinguishes from sibling tools like show_handwriting_canvas for fresh drawing. Mentions higher accuracy for specific use cases.

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 this when...' and 'Do NOT use this when...' scenarios, including an alternative tool name (show_handwriting_canvas). Covers both positive and negative cases thoroughly.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All fci-mcp tools Inspect

Try in Browser

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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.

Resources

Need Help?