Server Quality Checklist

Tool Scores

• generate_image_from_textA
  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 the full burden of behavioral disclosure. It mentions the action ('Generate') and output ('Path to the generated image file'), but does not cover critical aspects like rate limits, authentication needs, file formats, error handling, or whether the operation is idempotent, leaving significant gaps for a generative tool.

  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 with clear sections for Args and Returns, and each sentence adds value. It is appropriately sized for the tool's complexity, though it could be slightly more concise by integrating the technology mention into the main purpose statement.

  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 moderate complexity (generative AI operation), no annotations, and no output schema, the description provides basic purpose and parameter info but lacks details on behavioral traits, error cases, or output specifics beyond a path. It is minimally viable but has clear gaps in completeness.

  Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

  Parameters4/5

  Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

  The description adds meaningful context for the single parameter 'prompt' by explaining it as 'User's text prompt describing the desired image to generate', which goes beyond the schema's minimal title. With 0% schema description coverage and only one parameter, this adequately compensates, though it could include examples or constraints.

  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 specific action ('Generate an image'), resource ('based on the given text prompt'), and technology ('using Google's Gemini model'), distinguishing it from sibling tools like 'transform_image_from_encoded' and 'transform_image_from_file' which involve transformation rather than generation from text.

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

  Usage Guidelines3/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  The description implies usage for image generation from text prompts but does not explicitly state when to use this tool versus its siblings. It mentions the technology (Gemini model) which provides some context, but lacks explicit guidance on alternatives or exclusions.

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

• transform_image_from_fileA
  4.1/5.0
  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 mentions that the tool saves the transformed file on the server, which is useful context beyond basic functionality. However, it lacks details on permissions, rate limits, error handling, or what transformations are possible, leaving behavioral gaps.

  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 well-structured and front-loaded with the core purpose, followed by clear sections for Args and Returns. Every sentence earns its place by providing essential information without redundancy, making it efficient and easy to parse.

  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 complexity of an AI-based image transformation tool with no annotations and no output schema, the description is moderately complete. It covers the basic operation and return value but lacks details on supported image formats, transformation limits, or error cases, which are important for such a tool.

  Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

  Parameters4/5

  Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

  The description adds significant meaning beyond the input schema, which has 0% coverage. It explains that 'image_file_path' is for an existing image file to be transformed and 'prompt' describes the desired transformation, clarifying the purpose and usage of both parameters effectively.

  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 a specific verb ('Transform') and resource ('an existing image file'), and distinguishes it from siblings by specifying it works from a file path rather than text or encoded input. The phrase 'using Google's Gemini model' adds technical specificity.

  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 implicitly provides usage context by mentioning 'existing image file' and the Gemini model, which suggests when to use this tool. However, it doesn't explicitly state when to choose this over sibling tools like 'transform_image_from_encoded' or 'generate_image_from_text', missing explicit alternative guidance.

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

• transform_image_from_encodedA
  4.2/5.0
  Behavior3/5

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

  With no annotations provided, the description carries full burden. It discloses that the tool uses Google's Gemini model and saves the transformed image to the server, which are useful behavioral traits. However, it doesn't mention rate limits, authentication requirements, file size limits, or error conditions that would be important for a transformation tool.

  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 purpose statement followed by well-organized Args and Returns sections. Every sentence earns its place by providing essential information without redundancy. The formatting with clear section headers enhances readability.

  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?

  For a transformation tool with no annotations and no output schema, the description provides good parameter documentation and purpose clarity. However, it lacks information about the transformation process (e.g., quality, limitations, processing time), error handling, and more detailed behavioral context that would be valuable given the complexity of image transformation.

  Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

  Parameters5/5

  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 semantic information for both parameters. It specifies the exact format required for encoded_image (including header structure and supported formats) and explains that prompt describes 'desired transformation or modifications.' This adds significant value beyond the bare schema.

  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: 'Transform an existing image based on the given text prompt using Google's Gemini model.' It specifies the verb (transform), resource (existing image), method (Gemini model), and distinguishes from sibling tools (transform_image_from_file handles file input instead of encoded data).

  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 implicitly suggests usage context by mentioning 'existing image' and the encoding format, but doesn't explicitly state when to use this tool versus alternatives like transform_image_from_file or generate_image_from_text. It provides technical prerequisites but lacks comparative guidance.

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