fci-mcp

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

While annotations already include readOnlyHint=true, the description adds context about recognition families, canvas reuse behavior, and the prompt passthrough. No contradiction with annotations, but more detail on side effects (e.g., whether previous strokes are cleared) could be beneficial.

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 and front-loaded with the core purpose. It is longer than minimal but every sentence adds value. A slight reduction could be possible without losing clarity.

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 presence of an output schema and comprehensive annotations, the description covers all needed aspects: purpose, usage boundaries, parameter details, and behavioral rules. No obvious gaps remain.

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 description adds significant meaning beyond the schema: explains mode families, when to use force_new, the role of prompt, and language as optional hint. Each parameter is clearly contextualized.

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 FluxInk handwriting recognition canvas and explains its functionality. It explicitly distinguishes from sibling tools like recognize_image, create_layout, and show_style_canvas by giving 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.

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 instructions, including alternatives (e.g., use recognize_image for existing images). It also covers edge cases like not re-opening an existing canvas and when to set force_new.

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

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.

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.

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.

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.

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.

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.

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

Annotations already indicate readOnlyHint=true and destructiveHint=false. Description adds that the tool requires JSON-encoded style strokes from the canvas, clarifying its internal nature and prerequisites. 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?

Three tight sentences: first defines purpose, second gives usage warning and alternative, third adds prerequisite. No wasted words, front-loaded.

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 high schema coverage and presence of output schema, the description sufficiently covers purpose and usage boundaries. Lacks examples or error handling details, but adequate for an internal 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 description coverage is 100%, and the description does not provide additional details beyond the schema's parameter descriptions. 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 it is an internal endpoint for ink execution, invoked by the style canvas widget, and distinguishes from sibling show_style_canvas by warning not to call directly from chat.

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 says 'Do NOT call this tool directly from chat' and provides specific alternative: 'call show_style_canvas instead'. Also states it is invoked ONLY by the widget.

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