fci-mcp

fluxink-mcp-chatgpt-recognize-handwriting

Read-only

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.

Input Schema

TableJSON Schema

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

TableJSON Schema

Name	Required	Description	Default
`error`	No
`result`	No
`success`	Yes

TDQS

A4.3/5.0

Behavior3/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds that it is an execution endpoint but does not disclose additional behavioral details like rate limits or authentication requirements.

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, extremely concise, and front-loaded with the key purpose and usage constraint.

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 output schema present and the tool being internal, the description fully covers the necessary context: it clearly states the intended invocation path and payload requirement.

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 coverage is 100%, so baseline 3. The description mentions the stroke payload but does not add parameter-specific meaning beyond the schema. It does not compensate with extra details.

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 as an internal handwriting recognition endpoint, invoked only by the widget, and explicitly distinguishes from the sibling show_handwriting_canvas tool.

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 says 'Do NOT call this tool directly from chat' and redirects to show_handwriting_canvas, providing clear usage boundaries.

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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