Server Quality Checklist

Tool Scores

• edit_imageA
  4.4/5.0
  Behavior4/5

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

  With no annotations, the description carries the behavioral burden. It usefully explains mask transparency semantics (transparent areas are repainted, opaque areas preserved), accepted input formats, and a model limitation. It does not explicitly state that original files are left untouched or describe the exact output flow, but the schema's output_dir and return_preview parameters partially cover that.

  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 compact and well-structured: a one-sentence summary followed by numbered modes that map directly to input patterns. There is no filler, and the most decision-relevant information is front-loaded.

  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?

  For a 14-parameter tool with no annotations and no output schema, the description covers the main decision axes: mode selection, image formats, mask usage, and model constraints. Its main gaps are not explicitly routing to generate_image for new images and not describing the output/return behavior, but the rich parameter descriptions fill most of the remaining context.

  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?

  Schema description coverage is 71%, so the baseline is 3, but the description adds meaningful context for the core parameters: prompt+one image means whole edit, prompt+mask means inpainting, and prompt+multiple images means composition. It also adds the file-format restriction and the dall-e-3 constraint, which go 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 existing image(s) with a text instruction, and enumerates three distinct jobs: whole-image edit, inpainting, and composition/style reference. This differentiates it from sibling tools like generate_image (creating new images) and list_image_models (listing models).

  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 gives concrete when-to-use guidance by mapping each of the three modes to a specific input pattern: one image, image+mask, or multiple images. It also warns that dall-e-3 cannot edit and directs agents to a gpt-image model, but it does not explicitly contrast with generate_image for the from-scratch case.

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

• generate_imageA
  4.5/5.0
  Behavior4/5

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

  With no annotations present, the description carries the burden of disclosing side effects and behavior. It clearly states that images are saved to disk and that the tool returns file paths plus an inline preview. It also reveals model behavior around prompt-following quality. It does not disclose potential rate limits or authentication needs, but the core effects are transparent.

  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 three tight paragraphs with no filler. The first sentence front-loads the tool's core purpose and side effects, while the subsequent paragraphs add targeted, non-redundant guidance on prompt style and cost control. Every sentence contributes actionable information.

  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 (13 parameters, no output schema), the description covers the essential operational flow: generate, save, preview, and iterate. It also states return values, which matters because there is no output schema. It does not mention defaults like the output directory or naming convention, but these are well documented in the schema, so the description is sufficiently complete for correct tool selection and invocation.

  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?

  The input schema already has 100% coverage for all 13 parameters, so the baseline is 3. The description adds real value beyond the schema by teaching prompt-authoring strategy (subject, composition, lighting, palette, mood, quoted text) and by mapping quality choices to iteration phases. This moves it well above baseline.

  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 opens with a concrete, multi-part action: generate one or more images from a text prompt, save them to disk, and return file paths plus an inline preview. It names the model family (OpenAI GPT Image models) and clearly distinguishes the behavior from sibling tools like edit_image and list_image_models.

  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?

  It provides clear practical guidance on how to use the tool effectively: write long, specific prose describing subject, composition, lighting, and mood, and quote in-image text verbatim. It also gives a cost-control workflow (use low quality while iterating, high quality for the final render). It does not explicitly say 'use this instead of edit_image,' but the generation-focused context is clear.

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

• list_image_modelsA
  4.7/5.0
  Behavior4/5

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

  With no annotations, the description carries the burden of indicating behavior. 'Show' implies a read-only operation, and 'this API key can use' communicates API-key-scoped results. It does not explicitly state that no generation or editing side effects occur, but that is clear from context.

  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-loads the core purpose, and packs in both the output scope and the relevant invocation conditions. Every sentence earns its place with no repetition or fluff.

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

  Completeness5/5

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

  For a zero-parameter listing tool with no annotations and no output schema, the description is complete. It tells the agent what will be shown, the scope of the results, and when to invoke it, which is sufficient for correct use.

  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 has zero parameters, so there is no parameter documentation gap. The description does not need to add parameter-level meaning, and the baseline of 4 applies.

  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 specifies a clear verb and resource: 'Show the image models this API key can use.' It also states the tool provides guidance on selection, which makes its purpose distinct from the sibling tools generate_image and edit_image.

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

  Usage Guidelines5/5

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

  The description explicitly lists when to call the tool: when unsure a model is available, after a generation model error, or when the user asks what is possible. This gives an agent concrete decision criteria without needing to infer usage.

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