Server Quality Checklist

Tool Scores

• edit_imageA
  3.6/5.0
  Behavior2/5

  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 editing capabilities, it doesn't disclose important behavioral traits like whether edits are destructive to the original file, what permissions are needed, rate limits, output format, or error conditions. The description is insufficient for a mutation tool with zero annotation coverage.

  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?

  The description is efficiently structured with a clear opening sentence, bullet points for capabilities, and a practical usage instruction. Every sentence earns its place, and information is front-loaded with the core purpose stated first.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness2/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  For a complex image editing tool with 6 parameters, no annotations, and no output schema, the description is incomplete. It doesn't address behavioral aspects like file handling, error cases, or what the tool returns. While the schema covers parameters well, the overall context for proper tool invocation is insufficient.

  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?

  With 100% schema description coverage, the baseline is 3. The description adds some value by mentioning 'path to an existing image' (reinforcing image_path) and 'describe the changes you want' (reinforcing prompt), but doesn't provide additional semantic context beyond what's already well-documented in the 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?

  The description clearly states the tool's purpose with specific verbs ('edit an existing image using text prompts') and distinguishes it from siblings by focusing on editing existing images rather than generating new ones (generate_image) or composing multiple images (compose_images). The bullet points provide concrete examples of editing capabilities.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines4/5

  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 ('edit an existing image') and implies alternatives through sibling tool names, but doesn't explicitly state when NOT to use it or directly compare with compose_images/generate_image. The guidance is practical but lacks explicit exclusions.

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

• generate_imageA
  3.8/5.0
  Behavior3/5

  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 adds useful context about model capabilities (resolution, speed, thinking mode) and tips for effective prompting, but it does not disclose critical behavioral traits such as rate limits, authentication requirements, cost implications, or what happens on failure (e.g., error handling). The description is informative but incomplete for safe operation.

  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?

  The description is well-structured and appropriately sized, with a clear purpose statement followed by model details and tips. Every sentence earns its place by providing actionable information, though the tips section could be slightly more concise. It is front-loaded with the core functionality.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness3/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  Given the tool's complexity (image generation with multiple parameters) and lack of annotations or output schema, the description is moderately complete. It covers purpose, model options, and usage tips, but it lacks details on output format (e.g., file type, return structure), error conditions, and operational constraints like rate limits or costs, which are important for a generative AI tool.

  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 description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds marginal value by reinforcing prompt specificity and mentioning aspect ratio in the tips, but it does not provide additional semantic meaning beyond what the schema descriptions already cover (e.g., the schema's prompt description says 'Be descriptive and specific,' mirroring the tips). Baseline 3 is appropriate when the schema does the heavy lifting.

  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's purpose: 'Generate an image from a text prompt using Google Gemini's image generation.' This specifies the verb ('generate'), resource ('image'), and technology ('Google Gemini's image generation'), distinguishing it from sibling tools like 'compose_images' and 'edit_image' which imply different operations.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines4/5

  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 (image generation from text prompts) and includes tips for better results, but it does not explicitly state when to use alternatives like 'compose_images' or 'edit_image'. The model descriptions ('Best for high-volume tasks' vs 'Best for professional assets') offer some guidance, but no explicit exclusions or comparisons to siblings are provided.

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

• compose_imagesA
  4.2/5.0
  Behavior4/5

  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 adds valuable context beyond the schema by specifying model-specific constraints (e.g., 'nano-banana supports up to 3 input images') and use-case examples. It does not mention permissions, rate limits, or output format, but provides practical operational details.

  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?

  The description is appropriately sized and front-loaded: the first sentence states the core purpose, followed by model constraints and use cases. Every sentence earns its place by providing essential information without redundancy or fluff.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness4/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  Given the tool's complexity (6 parameters, no annotations, no output schema), the description is mostly complete. It covers purpose, constraints, and use cases well, but lacks details on output behavior (e.g., file format, error handling) and does not fully compensate for the absence of annotations and output schema.

  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 description coverage is 100%, so the schema already documents all parameters thoroughly. The description does not add any parameter-specific details beyond what the schema provides (e.g., it doesn't explain 'prompt' or 'image_paths' further). Baseline 3 is appropriate when the schema does the heavy lifting.

  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's purpose: 'Combine multiple images into a new composition.' It uses specific verbs ('combine') and resources ('images'), and distinguishes from siblings like 'edit_image' (modify existing) and 'generate_image' (create from scratch) by focusing on multi-image composition.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines4/5

  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 through the 'Great for:' section, listing specific use cases like product mockups and fashion photos. However, it does not explicitly state when NOT to use it or name alternatives among sibling tools, which prevents a perfect score.

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