Server Quality Checklist

Tool Scores

• generate_imageA
  3.5/5.0
  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 model selection and creation capabilities, it lacks critical information about rate limits, authentication requirements, cost implications, response format, or error handling. For a generative AI tool with potential costs and limitations, 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.

  Conciseness5/5

  Is the description appropriately sized, front-loaded, and free of redundancy?

  The description is exceptionally concise and well-structured in a single sentence that communicates the core capability, technology stack, and key feature. Every element earns its place: the emoji adds visual context, 'Create stunning AI-generated images' states the purpose, 'using Azure DALL-E 3 or FLUX models' specifies technology, and 'with intelligent model selection' highlights differentiation. No wasted words.

  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 (AI image generation with multiple models and parameters) and lack of both annotations and output schema, the description is incomplete. While concise and clear about purpose, it doesn't address behavioral aspects like cost, rate limits, or response format that are crucial for such a tool. The excellent schema coverage helps, but the description alone doesn't provide sufficient context for safe and effective use.

  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?

  The description provides no parameter-specific information beyond what's already comprehensively documented in the input schema (100% coverage). The schema includes detailed descriptions, examples, enums, and defaults for all parameters. The description adds no additional semantic context about parameters, so it meets but doesn't exceed the baseline expectation when schema coverage is complete.

  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 ('Create stunning AI-generated images') and resources ('using Azure DALL-E 3 or FLUX models'). It distinguishes the tool's unique capability of 'intelligent model selection' which adds differentiation even without sibling tools. The description goes beyond just restating the name by specifying the technology and key feature.

  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 context through phrases like 'intelligent model selection' and mentions of specific models, but provides no explicit guidance on when to use this tool versus alternatives. There are no sibling tools mentioned, so the lack of comparative guidance is understandable, but it doesn't offer any when/when-not advice or prerequisites for successful use.

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