AWS Nova Canvas MCP Server

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. While it states the tool 'automatically' removes backgrounds, it doesn't disclose important behavioral traits: what permissions or authentication might be needed, whether the operation is destructive to the original file, what formats are supported, whether there are rate limits, or what happens if the background removal fails. For a tool that modifies images, this represents significant gaps in behavioral transparency.

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 appropriately concise with three clear sections: purpose statement, parameters, and return value. Each sentence earns its place, though the 'ctx: MCP context' parameter documentation adds little value since it's not in the actual schema. The structure is front-loaded with the core purpose first, making it easy to understand quickly.

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 (image processing with potential side effects), the complete lack of annotations, no output schema, and minimal parameter documentation, the description is insufficiently complete. It doesn't explain what 'automatically' means in practice, doesn't describe the return dictionary structure beyond mentioning it contains a file path, and provides no context about error conditions or limitations. For an image manipulation tool with behavioral implications, this represents significant incompleteness.

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 description adds minimal parameter semantics beyond what the schema provides. It mentions 'image_path: File path of the original image' which slightly clarifies the schema's 'Image Path' title, but with 0% schema description coverage and only 1 parameter, the description doesn't provide format requirements, path conventions, or supported image types. The baseline for 0% coverage with 1 parameter would be higher if the description compensated more effectively.

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 with a specific verb ('Remove') and resource ('background of an image'), making it immediately understandable. It distinguishes itself from siblings like 'inpainting' or 'outpainting' by focusing specifically on background removal rather than general image editing. However, it doesn't explicitly differentiate from all siblings in the description text itself.

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 no guidance on when to use this tool versus alternatives. With siblings like 'inpainting' and 'outpainting' that might also involve background manipulation, there's no indication of when background_removal is the appropriate choice versus those other tools. The description lacks any 'when to use' or 'when not to use' context.

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 full burden but provides minimal behavioral context. It mentions the tool generates images but doesn't disclose computational requirements, quality expectations, generation time, rate limits, or authentication needs. The return format is mentioned but without details about error conditions or file format.

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 purpose statement followed by organized parameter explanations. Every sentence adds value, though the 'ctx: MCP context' parameter explanation is minimal. The structure helps an agent understand the tool's interface efficiently.

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?

For a 7-parameter image generation tool with no annotations and no output schema, the description provides good parameter coverage but lacks important behavioral context. It doesn't explain what happens when generation fails, file format of outputs, or performance characteristics. The return statement is minimal ('Dict containing file path') without error handling details.

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 description provides excellent parameter semantics despite 0% schema description coverage. It explains each parameter's purpose clearly: 'prompt' describes the image, 'colors' specifies hex codes with quantity guidance, 'reference_image_path' is optional, 'negative_prompt' excludes attributes, 'height/width' are pixel dimensions, and 'cfg_scale' controls prompt matching degree. This fully compensates for the schema's lack of 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 'Generate an image using a specified color palette' which is a specific verb+resource combination. It distinguishes from siblings like 'text_to_image' by emphasizing color guidance, but doesn't explicitly contrast with other image generation siblings like 'image_variation' or 'inpainting'.

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?

No guidance on when to use this tool versus alternatives like 'text_to_image' or 'image_variation' is provided. The description only states what the tool does, not when it's appropriate versus other image generation methods available in the sibling tool list.

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 full burden for behavioral disclosure. While it mentions the tool 'Generates an image' (implying creation/mutation), it doesn't disclose important behavioral traits like whether this is a computationally intensive operation, potential rate limits, authentication requirements, or what happens if the reference image is invalid. The description provides basic functional information but lacks operational 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?

The description is well-structured with a clear purpose statement followed by organized parameter explanations. Every sentence serves a purpose: the first states the tool's function, and subsequent lines document parameters and return value. It could be slightly more concise by combining some parameter explanations, but overall it's efficiently organized with zero wasted text.

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?

For a 7-parameter image generation tool with no annotations and no output schema, the description provides adequate functional coverage but lacks operational context. It explains what the tool does and what parameters mean, but doesn't address important considerations like performance characteristics, error conditions, or format details of the returned dictionary. The absence of output schema means the description should ideally explain the return structure more thoroughly.

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 0% schema description coverage (titles only, no descriptions), the description provides substantial value by explaining all 7 parameters. It clarifies that 'image_path' is for the reference image, 'prompt' describes what to generate, 'negative_prompt' excludes attributes, 'control_mode' specifies the technique (with CANNY_EDGE as example), and provides context for dimensions and cfg_scale. This compensates well for the schema's lack of 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 with a specific verb ('Generate an image') and resource ('that follows the layout and composition of a reference image'). It distinguishes from siblings like 'text_to_image' (no reference image) and 'image_variation' (likely modifies existing images rather than generating new ones based on reference composition).

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 implies usage context through the phrase 'follows the layout and composition of a reference image,' suggesting this tool should be used when you want to generate new images with similar structure to an existing image. However, it doesn't explicitly state when NOT to use it or name specific alternatives among the sibling tools, leaving some ambiguity about tool selection.

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?

No annotations are provided, so the description carries full burden. It mentions the tool 'generates' variations, implying creation/mutation, but doesn't disclose behavioral traits like whether it overwrites files, requires specific permissions, has rate limits, or what happens with invalid inputs. The description adds minimal behavioral context beyond the basic action.

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, Args, Returns) and uses bullet-like formatting. Every sentence earns its place, though the parameter explanations could be slightly more concise. The front-loaded purpose statement 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?

For a 7-parameter mutation tool with no annotations and no output schema, the description provides good parameter semantics but lacks crucial behavioral context. It doesn't explain the return value format beyond 'Dictionary containing the file path', nor does it cover error conditions, side effects, or usage constraints relative to siblings.

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 0% schema description coverage, the description fully compensates by providing clear semantic explanations for all 7 parameters. Each parameter gets a brief but meaningful description that adds value beyond the schema's titles (e.g., 'Similarity between the original image and the generated image' for similarity_strength, 'Prompt matching degree' for cfg_scale).

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 a new variation of the input image while maintaining its content.' This specifies the verb ('generate'), resource ('variation of the input image'), and key constraint ('maintaining its content'). However, it doesn't explicitly differentiate from sibling tools like 'inpainting' or 'outpainting' which also modify images.

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 no guidance on when to use this tool versus alternatives. With siblings like 'text_to_image', 'inpainting', and 'outpainting' available, there's no indication of when image variation is preferred over these other image generation or modification tools.

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 full burden for behavioral disclosure. It mentions the 'open_browser' behavior which is useful, but fails to describe important aspects like whether this is a read-only or destructive operation, what permissions are needed, whether it modifies the original file, rate limits, or error conditions. For an 8-parameter image manipulation tool, this leaves significant gaps.

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, Args, Returns) and front-loads the core functionality. Most sentences earn their place, though the Returns section could be slightly more detailed given there's no output schema. The formatting with bullet-like sections enhances readability.

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?

For an 8-parameter image manipulation tool with no annotations and no output schema, the description provides adequate but incomplete coverage. The parameter explanations are strong, but missing behavioral context (permissions, side effects) and the return value description is minimal ('Dict: Dictionary containing the file path'). Given the complexity, more detail on output structure and behavioral implications would be beneficial.

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 0% schema description coverage, the description compensates well by providing clear explanations for all 8 parameters in the Args section. Each parameter gets a brief but meaningful description that adds value beyond the schema's titles (e.g., explaining 'cfg_scale' as 'Image matching degree for the prompt (1-20)' and giving examples for 'mask_prompt'). The only minor gap is that 'image_path' could specify expected formats.

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 with specific verbs ('inpaint a specific part of an image') and resources ('image', 'text mask prompt'), distinguishing it from siblings like 'background_removal' or 'outpainting' which involve different image manipulation operations. The opening sentence provides a concise yet complete statement of function.

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 no guidance on when to use this tool versus alternatives like 'outpainting' or 'image_variation', nor does it mention any prerequisites or constraints. While the purpose is clear, there's no context about appropriate use cases or comparisons with sibling tools.

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 full burden for behavioral disclosure. While it implies a generative/mutation operation ('expand', 'create'), it doesn't disclose important behavioral traits: whether this is a destructive operation, what permissions are needed, whether there are rate limits, what happens if generation fails, or what the typical processing time might be. The description adds minimal behavioral context beyond the basic operation.

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 appropriately sized. It begins with a clear purpose statement, then provides organized parameter documentation in bullet-like format. Every sentence earns its place by either stating the purpose or explaining parameters. It could be slightly more concise by combining some parameter explanations, but overall it's 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 the tool's complexity (8 parameters, generative operation) and lack of both annotations and output schema, the description is moderately complete. It thoroughly documents parameters but lacks behavioral context and output details. The return value is minimally described ('Dictionary containing the file path'), but without an output schema, more detail about the response structure would be helpful for a complex tool like this.

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 0% schema description coverage, the description fully compensates by providing detailed parameter documentation. It explains all 8 parameters with clear semantic meaning beyond just their names: 'File path of the original image', 'Text describing the content to be generated', 'Prompt matching degree (1-20)', etc. This adds significant value over the bare 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: 'Expand the image to create an outpainting.' This specifies the verb ('expand') and resource ('image'), and distinguishes it from sibling tools like 'inpainting' (which modifies existing areas) or 'text_to_image' (which creates from scratch). However, it doesn't explicitly contrast with all siblings, such as 'image_variation'.

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 no guidance on when to use this tool versus alternatives. It doesn't mention when outpainting is appropriate compared to inpainting, image_variation, or other image manipulation tools. There are no usage prerequisites, exclusions, or named alternatives provided.

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 mentions a maximum size constraint ('The maximum size is 1048578') and input types, but lacks critical details such as authentication needs, rate limits, error handling, or whether the operation is read-only or mutative. For a tool that processes images, this leaves significant gaps in understanding its behavior.

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 concise, with a clear purpose statement, key constraints, and a formatted Args/Returns section. Every sentence adds value, such as specifying input types and output. It could be slightly more front-loaded by emphasizing the thumbnail creation first, but overall it's efficient and easy to parse.

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 moderate complexity (3 parameters, no output schema, no annotations), the description is adequate but incomplete. It covers the basic operation and parameters but misses details like return format (beyond 'Image'), error cases, or performance implications. For a tool without annotations or output schema, it should provide more behavioral context to be fully helpful.

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 description adds meaningful context beyond the input schema, which has 0% description coverage. It explains that 'image_path' can be a URL or local file path, clarifies 'width' and 'height' as output dimensions in pixels, and notes defaults implicitly via the schema. However, it doesn't detail constraints like valid ranges for dimensions or file formats, slightly limiting its utility.

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: 'Create a thumbnail of the image and return it.' It specifies the verb ('create'), resource ('thumbnail of the image'), and outcome ('return it'). However, it doesn't explicitly differentiate from sibling tools like 'image_variation' or 'text_to_image', which might also involve image processing, so it falls short of a perfect score.

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 minimal usage guidance: it mentions support for URLs or local file paths, but offers no advice on when to use this tool versus alternatives like 'image_variation' or 'inpainting'. There's no context on prerequisites, limitations, or scenarios where this tool is preferred, leaving the agent with little direction.

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. It discloses that the tool generates images and mentions the show_image follow-up, but lacks details about behavioral traits like rate limits, authentication needs, generation time, supported image formats, or what happens when limits are exceeded (e.g., maximum character counts). The description doesn't contradict any annotations since none exist.

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 appropriately sized. It starts with the core purpose, provides a usage tip, then details all parameters with clear explanations, and ends with return information. Every sentence adds value with no redundancy or fluff.

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 complexity (8 parameters, no annotations, no output schema), the description does well by explaining all parameters and mentioning the return format. However, it could be more complete by addressing behavioral aspects like generation constraints, error conditions, or format details. The lack of output schema means the description should ideally explain return values more thoroughly.

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 0%, so the description must compensate fully. It successfully adds meaning for all 8 parameters beyond the schema's basic titles, providing character limits for prompts, pixel ranges for dimensions, numerical ranges for cfg_scale and num_images, and explanations for seed and open_browser. This significantly enhances understanding beyond the minimal 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 'Generate an image from a text prompt' which is a specific verb+resource combination. It distinguishes from sibling tools like background_removal, color_guided_generation, and image_variation by focusing on text-to-image generation rather than image manipulation or variation.

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 by mentioning 'After generation, you can use the show_image tool to view the thumbnail,' which gives a next-step recommendation. However, it doesn't explicitly state when to use this tool versus alternatives like image_variation or color_guided_generation, nor does it provide exclusion criteria.

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