Server Quality Checklist

Tool Scores

• analyze_imageA
  4.4/5.0
  Behavior4/5

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

  Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds behavioral context: it calls an OpenAI-compatible vision API, accepts various image input forms (URL, local path, base64), and returns text/Markdown. This adds value beyond annotations without contradiction.

  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: a clear opening sentence, a bulleted list of use cases, and a section explaining arguments. It is mostly concise, though some examples could be trimmed. It front-loads the most important information.

  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?

  The description covers all essential aspects: input formats, prompt style, optional MIME override, and return type. Given that an output schema exists, the description effectively complements it by explaining the return value as 'plain text / Markdown.' This provides a complete picture.

  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?

  Although the schema has detailed descriptions for each parameter (image_source, prompt, mime_type), the tool description adds extra context with examples and guidance on prompt specificity. This enhances the schema's information, earning a score 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 clearly states that the tool analyzes an image using a multimodal LLM, and lists specific use cases (describe contents, OCR, charts, UI screenshots, etc.). It is specific about the action and resource, and distinguishes itself from siblings (none listed).

  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 context by listing when to use the tool (e.g., whenever you have an image and need to describe, extract text, analyze charts). It does not explicitly state when not to use or mention alternatives, but given the absence of sibling tools, this is acceptable.

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