Server Quality Checklist

Tool Scores

• edit_imageA
  3.7/5.0
  Behavior2/5

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

  No annotations are provided, so the description must disclose behavioral traits. It mentions the model and use cases but does not explain whether the original image is preserved, what the return format is, or any side effects. This is insufficient for a mutation 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 concise, with two sentences that front-load the purpose. Every word earns its place, and the structure is clear and efficient.

  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?

  The description covers purpose and basic usage but lacks details on return values, side effects, and explicit alternative guidance. Given no output schema and no annotations, the description should provide more context to be fully complete.

  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 coverage is 100%, so the schema already documents all parameters. The description reinforces the key parameters (image_path, prompt) but adds no additional semantic detail beyond the 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 edits an existing image using text instructions via FLUX.1-Kontext, with specific use cases. It distinguishes itself from sibling tools like generate_image by emphasizing 'existing image'.

  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?

  Provides clear usage context with examples like mockups, style transfer, and contextual editing, implying when to use the tool. However, it does not explicitly name alternatives or exclusion criteria relative to generate_image.

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

• generate_imageA
  4/5.0
  Behavior3/5

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

  With no annotations, the description carries the transparency burden. It discloses the core behavior (generation, saving, and returning a file path), but omits details about where the file is saved, whether it overwrites existing files, or network/API dependencies (e.g., NIM service). This is adequate but leaves gaps for a tool with side effects.

  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 two sentences long, front-loaded with the primary action and return value. It avoids redundancy and every sentence contributes useful information, making it highly concise and easy to parse.

  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 full schema and no output schema, the description covers the essential context: what the tool does, what it returns, and model selection tradeoffs. It does not explain where the saved image is stored or how to access it, but the schema covers parameter semantics sufficiently, so the description is largely complete for an agent to invoke the 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?

  The input schema already provides detailed descriptions for all six parameters (100% coverage), including examples and defaults. The description's mention of model quality duplicates the schema's 'model' parameter description, adding no new semantic meaning beyond what the schema already offers.

  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 'Generate an image from a text prompt using NVIDIA NIM FLUX models', specifying a concrete action and resource. It also distinguishes the tool from siblings (list_models and edit_image) by focusing on creation rather than listing or modifying images.

  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 in-tool guidance on model selection ('Use 'flux-1-dev' for high quality or 'flux-1-schnell' for speed'), which helps agents choose the right model. However, it does not explicitly compare when to use generate_image versus the sibling tools (list_models, edit_image), so it lacks exclusions or alternative tool references.

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

• list_modelsA
  4.2/5.0
  Behavior3/5

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

  With no annotations, the description carries the full burden. It communicates a safe, read-only 'List' operation with no side effects, but does not disclose potential return format, pagination, or what 'capabilities' means specifically. This is acceptable but leaves room for more behavioral detail.

  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 a single, front-loaded sentence that immediately states the action and resource. Every word is useful, and there is no redundancy or filler.

  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 simplicity (no parameters, no annotations, no output schema), the description gives enough context: it names the resource and the type of output (available models + capabilities). It could be more explicit about the return format, but the simple nature makes it largely complete.

  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 tool accepts zero parameters, and the schema coverage is 100% (empty schema). Per the baseline for no parameters, a score of 4 is appropriate. The description adds no parameter-specific information because there are no parameters to describe, and it correctly references the output content.

  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 uses a specific verb ('List') and identifies the exact resource ('available FLUX models on NVIDIA NIM') plus additional value ('their capabilities'). This clearly distinguishes it from sibling tools like generate_image and edit_image, which perform actions rather than listing.

  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 implies the tool should be used when an agent needs to know which FLUX models are available and what they can do. While it doesn't explicitly exclude alternatives or mention sibling tools, the context is clear and unambiguous for a listing tool.

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