Server Quality Checklist

Tool Scores

• imageA
  4.1/5.0
  Behavior4/5

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

  With no annotations provided, the description carries full burden and does well by disclosing key behavioral traits: foreground/background capture options, output methods (file path or Base64), AI analysis when question provided, window shadow/frame exclusion, and auto-selection of AI providers. It misses some details like rate limits or specific error conditions.

  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 appropriately sized and front-loaded with core functionality, though it could be slightly more concise. Every sentence adds value: first states purpose, second details targets, third covers capture modes, fourth explains output, fifth describes analysis, sixth notes exclusions, seventh lists AI providers. Minor redundancy with schema exists.

  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 5-parameter tool with no annotations and no output schema, the description provides good coverage of functionality, behavior, and context. It explains capture targets, focus behavior, output formats, and AI analysis integration. It could improve by detailing response structure or error handling, but it's largely complete for the tool's 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%, so the schema already documents all 5 parameters thoroughly. The description mentions parameters like 'app_target', 'format', and 'question' but doesn't add significant meaning beyond what the schema provides. Baseline 3 is appropriate when schema does heavy lifting.

  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 captures macOS screen content and optionally analyzes it, specifying targets (entire screen, specific app window, all windows of an app) and output methods. It distinguishes from sibling tools 'analyze' and 'list' by focusing on capture functionality with optional analysis, not pure analysis or 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 provides clear context for when to use the tool (capturing screen content with optional AI analysis) and implies alternatives through sibling tools, but doesn't explicitly state when not to use it or directly compare to 'analyze' or 'list'. It gives practical guidance on target selection and output formats.

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

• analyzeA
  4.3/5.0
  Behavior4/5

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

  With no annotations provided, the description carries full burden and does well by explaining capabilities (image understanding, OCR, flexible AI configuration), supported file types, and example usage. It mentions server configuration dependencies but could be more explicit about potential limitations like image size constraints or rate limits.

  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 appropriately sized and well-structured with clear sections: purpose statement, usage context, capabilities list, and example. The capabilities section could be more concise, but overall it's front-loaded with essential information and every sentence adds value.

  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 tool with 3 parameters, 100% schema coverage, but no annotations or output schema, the description does well by explaining capabilities, usage context, and providing concrete examples. It could be more complete by mentioning potential error cases or response format expectations, but covers most essential aspects given the 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%, so the baseline is 3. The description adds some value by explaining the purpose of parameters in context (e.g., 'question' parameter examples, 'provider_config' flexibility), but doesn't provide significant additional semantics beyond what's already well-documented 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 clearly states the tool analyzes pre-existing image files using AI models, specifying the resource (image files) and verb (analyze). It distinguishes from sibling tools 'image' and 'list' by focusing on AI-powered analysis rather than basic image operations or listing functions.

  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 states when to use this tool: 'when an image already exists (e.g., previously captured, downloaded, or generated) and you need to understand its content, extract text, or answer specific questions about it.' It provides clear context for usage without needing to mention specific exclusions since the scope is well-defined.

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

• listA
  4.3/5.0
  Behavior4/5

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

  With no annotations provided, the description carries the full burden of behavioral disclosure and does so effectively. It explains the tool's capabilities in detail, mentions fuzzy matching for app identification, describes optional window details, and specifies default behaviors (item_type inference). However, it doesn't mention potential limitations like performance impact, permission requirements, or error conditions, preventing a perfect score.

  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 with clear sections (Capabilities, Use Cases) and uses bullet points effectively. While somewhat lengthy, every sentence serves a purpose: explaining capabilities, providing examples, and demonstrating workflow integration. The version information at the end could be considered extraneous but doesn't significantly detract from the overall clarity.

  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 tool with 3 parameters, 100% schema coverage, but no output schema or annotations, the description provides substantial context. It explains all three item_type options with practical examples, demonstrates parameter combinations, and shows how results feed into other tools. The main gap is the lack of output format description, which would be helpful given the absence of an output schema.

  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?

  With 100% schema description coverage, the baseline is 3. The description adds some value by providing concrete examples in the Use Cases section (e.g., 'Photoshop', 'Notes') and explaining the practical meaning of window details like 'bounds' for image capture. However, it doesn't significantly enhance parameter understanding beyond what's already documented in the comprehensive schema descriptions.

  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's purpose: 'Lists various system items on macOS, providing situational awareness.' It specifies three distinct capabilities (running applications, application windows, server status) and distinguishes itself from sibling tools like 'analyze' and 'image' by focusing on listing rather than analysis or image capture. The description goes beyond the name 'list' to explain what specific items can be enumerated.

  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 usage guidance through the 'Capabilities' and 'Use Cases' sections. It clearly indicates when to use each item_type: checking if an app is running before automation, finding specific windows for image capture, and getting server status. The description also references sibling tools ('image') for follow-up actions, creating a clear workflow context.

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