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

With no annotations provided, the description carries full burden and does well: it discloses that images are saved to files with paths returned, emphasizes the critical workspace_dir requirement, and provides detailed prompt best practices and negative prompt guidance. It doesn't mention rate limits, authentication needs, or error conditions, but covers core behavioral aspects adequately for a generation tool.

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: purpose statement, important note, prompt best practices, examples, and return type. It's appropriately sized for a complex tool with 10 parameters. Some redundancy exists (e.g., repeating that it uses Amazon Nova Canvas), but overall it's efficient and front-loaded with critical information.

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 (10 parameters, generation operation) and the presence of an output schema (McpImageGenerationResponse), the description is complete. It covers purpose, critical usage requirements, detailed prompt guidance, examples, and return information. With no annotations, it adequately compensates by explaining behavioral aspects and best practices for effective use.

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%, so the schema already documents all 10 parameters thoroughly. The description adds some value by explaining prompt best practices and negative prompt usage, which helps understand the 'prompt' and 'negative_prompt' parameters beyond their schema descriptions. However, it doesn't add significant semantic context for other parameters like width, height, or seed beyond what the schema provides.

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: 'Generate an image using Amazon Nova Canvas with text prompt' and 'The generated image will be saved to a file and the path will be returned.' This specifies the verb (generate), resource (image), technology (Amazon Nova Canvas), and outcome (saved file with returned path). It distinguishes from the sibling tool 'generate_image_with_colors' by not mentioning color parameters.

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 clear context for when to use this tool: for image generation based on text prompts using Amazon Nova Canvas. It includes an 'IMPORTANT FOR ASSISTANT' section about always providing workspace_dir. However, it doesn't explicitly contrast when to use this vs. the sibling 'generate_image_with_colors' tool, which would be needed for a perfect score.

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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behaviors: the tool generates and saves images, returns file paths, and requires workspace_dir for accessibility. It also mentions prompt best practices and example color schemes. However, it doesn't cover potential limitations like rate limits, error conditions, or performance characteristics, which would be helpful for a complex tool with 11 parameters.

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 (purpose, important note, best practices, examples, returns) and is appropriately sized for a complex tool. However, it includes some redundancy (e.g., repeating the importance of workspace_dir) and could be more front-loaded by moving critical usage instructions earlier. Every sentence adds value, but minor trimming could improve efficiency.

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 (11 parameters, no annotations, but with output schema), the description is complete enough. It covers purpose, usage, behavioral context, and examples, and the output schema handles return values. The description compensates for the lack of annotations by providing practical guidance and warnings, making it sufficient for an agent to use the tool effectively.

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?

The schema description coverage is 100%, so the schema already documents all 11 parameters thoroughly. The description adds minimal parameter-specific semantics beyond the schema—it emphasizes the importance of workspace_dir and provides example color values, but doesn't explain the meaning or interaction of parameters like cfg_scale, seed, or quality. Given the high schema coverage, a baseline score of 3 is appropriate as the description adds some value but relies heavily on 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 purpose: 'Generate an image using Amazon Nova Canvas with color guidance.' It specifies the verb ('generate'), resource ('image'), and technology ('Amazon Nova Canvas'), and distinguishes it from the sibling tool 'generate_image' by emphasizing color guidance. The description goes beyond the name to explain the core functionality.

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 usage guidelines, including when to use this tool (for image generation with color guidance) and critical instructions like 'Always send the current workspace directory when calling this tool!' It also offers prompt best practices and distinguishes from alternatives by noting the sibling tool 'generate_image' exists, though it doesn't explicitly say when to choose one over the other. The guidance is comprehensive and actionable.

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