Server Quality Checklist

Tool Scores

• describe_imageA
  4.6/5.0
  Behavior5/5

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

  No annotations are present, so the description carries the full burden of disclosing behavior. It specifies the model used (VLM), return format (text description, one section per image), accepted input formats (paths, URLs, data URIs, base64), and nuances like 'relative paths resolve against the server working directory' and 'detail ... forwarded only by the OpenAI-compatible provider.' This goes well beyond the bare minimum.

  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 longer than average but well-organized: it begins with the core purpose, then covers when to use, input formats, and optional parameters. Every section earns its place; the only minor deduction is for slight redundancy with the schema, which could be trimmed.

  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?

  With no output schema, no annotations, and no sibling tools, the description still fully equips an agent: it explains input formats, output structure ('the reply contains one section per image'), parameter overrides, and provider-specific behavior. This is more than sufficient for a tool of this complexity.

  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%, and each parameter already includes a descriptive comment in the schema. The description largely restates this information (e.g., 'question for a targeted question (overrides task)' matches the schema's 'Overrides task') rather than adding entirely new semantic detail. It does offer minor clarifications like the array meaning 'several images together,' but this is also already in 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 opens with a specific verb+resource: 'Analyze one or more images with a vision-language model (VLM) and return a text description.' It clearly distinguishes when this tool is needed (when an image cannot be attached directly to the conversation) and lists concrete use cases, which effectively differentiates it from any hypothetical alternative.

  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 tells agents when to use the tool: 'USE THIS TOOL when an image cannot be attached directly to the conversation' followed by a comprehensive list of scenarios (screenshots, error dialogs, charts, etc.). This provides clear context and practical guidance without needing to name sibling tools.

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