Server Quality Checklist

Tool Scores

• analyze_images_batchA
  3.9/5.0
  Behavior3/5

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

  Annotations already declare readOnlyHint=true and destructiveHint=false, establishing non-destructive read behavior. The description adds value by specifying the underlying model (Google Gemini Vision), privacy alignment, and supported input types (local paths or URLs). It does not, however, disclose rate limits, authentication needs, or potential side effects beyond what annotations convey.

  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 concise sentences, front-loading the core purpose and use cases. No unnecessary words or repetition. Every sentence adds value.

  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 tool has no output schema, so the description should clarify what the tool returns (e.g., analysis text or structured results). It omits this, leaving the agent to infer. While the purpose is clear, the lack of return value specification is a noticeable gap for a tool performing batch analysis.

  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% with detailed descriptions for all 4 parameters. The description reiterates that inputs can be local paths or URLs and can be mixed, but this adds minimal new meaning beyond the schema. Baseline 3 is appropriate since the schema already handles parameter semantics thoroughly.

  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 'Batch analyze multiple images (2-5) using Google Gemini Vision,' specifying verb and resource with a quantity range. It lists explicit use cases (comparing screenshots, before/after, multi-page documents) and differentiates from the sibling tool 'analyze_image_with_gemini' which handles single 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 implies when to use: for multiple images, comparisons, or multi-page documents. It references 'Same privacy rules as analyze_image_with_gemini', providing context. However, it does not explicitly state when not to use or list alternative tools beyond the sibling.

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

• analyze_image_with_geminiA
  4.4/5.0
  Behavior5/5

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

  Annotations indicate readOnlyHint=true and destructiveHint=false. The description adds key behavioral context: image bytes are sent to Google Gemini API, URL images are downloaded first, and consent is required. This goes beyond annotations without contradicting them.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness3/5

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

  The description is verbose, especially the 'PRIVACY RULES' section which repeats consent requirements. While front-loaded with the main purpose, the detailed rules could be condensed. Some sentences could be combined without losing clarity.

  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 input and behavioral aspects well, but does not explain the output/return format. Since there is no output schema, the agent lacks information on what the tool returns (e.g., text, JSON). This is a significant gap for a tool that produces results.

  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 has 100% coverage with descriptions for all 4 parameters. The description adds meaningful examples (e.g., image_source paths, prompt text, model overrides) and explains the special behavior of confirm_send_to_gemini. This adds significant value beyond the schema.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose4/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  The description clearly states the tool analyzes images using Gemini Vision, but does not explicitly differentiate from the sibling tool 'analyze_images_batch' (which presumably handles batch processing). The purpose is specific but could be clearer about scope.

  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 provides explicit when-to-use (only when main model lacks vision) and when-not-to-use (main model has vision, unless user requests). It also mandates user consent before calling with confirm_send_to_gemini=true. These guidelines are thorough and actionable.

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