Server Quality Checklist

Tool Scores

• get_blender_sceneA
  3.5/5.0
  Behavior3/5

  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. It states the tool retrieves scene information and specifies the return format, which is helpful. However, it doesn't cover other behavioral aspects like error conditions, performance implications, or whether it's read-only (implied but not explicit). The description adds some value but lacks comprehensive behavioral details.

  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 highly concise and well-structured. It uses two brief sentences: one for the purpose and one for the return format, with no wasted words. The information is front-loaded, making it easy for an agent to parse quickly.

  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 simplicity (0 parameters, no annotations, but with an output schema), the description is reasonably complete. It explains what the tool does and the return format, which aligns with the output schema's role. However, it could be more comprehensive by addressing usage context or behavioral nuances, slightly reducing completeness.

  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 0 parameters, and the input schema has 100% description coverage (though empty). The description doesn't need to explain parameters, so it appropriately focuses on the tool's function and output. This meets the baseline for tools with no parameters, as there's nothing to compensate for.

  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's purpose: 'Get information about the current Blender scene.' It specifies the verb ('Get') and resource ('current Blender scene'), making the function unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'blender_exec' or 'image_to_3d_model', which prevents a perfect score.

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

  Usage Guidelines2/5

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

  The description provides no guidance on when to use this tool versus alternatives. It doesn't mention contexts where it's appropriate, prerequisites, or comparisons to sibling tools like 'blender_exec' or 'image_to_3d_model'. This lack of usage context leaves the agent with minimal direction.

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

• image_to_3d_modelA
  3.8/5.0
  Behavior3/5

  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. It describes the core behavior (color extraction and 3D model creation) and output format (JSON with status, colors, model info), but lacks details on error handling, performance (e.g., processing time), side effects (e.g., file creation in Blender), or dependencies (e.g., Blender installation). It doesn't contradict annotations, but could be more comprehensive.

  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 well-structured and front-loaded: the first sentence states the core purpose, followed by a process explanation, then a clear 'Args' and 'Returns' section. Every sentence adds value without redundancy, and the bullet-like formatting enhances readability while remaining concise.

  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 moderate complexity (3 parameters, no annotations, but with an output schema), the description is fairly complete. It covers purpose, parameters, and return values, and the output schema reduces the need to detail JSON structure. However, it lacks context on integration with Blender (e.g., scene management) and error cases, leaving some gaps.

  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 0%, so the description must compensate. It adds meaningful semantics for all three parameters: 'image_data' (Base64-encoded, can include data URL), 'model_type' (shape options with default), and 'model_name' (naming with default). This goes beyond the schema's basic titles and types, providing practical usage context.

  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 with specific verbs ('create a 3D model in Blender') and resources ('with colors extracted from an image'), distinguishing it from sibling tools like 'blender_exec' (generic execution) and 'get_blender_scene' (retrieval). It explains the two-step process: color extraction from image and 3D model creation with matching materials.

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

  Usage Guidelines2/5

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

  The description provides no guidance on when to use this tool versus alternatives like 'blender_exec' or other 3D modeling approaches. It mentions the tool's function but lacks context about prerequisites (e.g., Blender availability), use cases (e.g., prototyping, visualization), or limitations (e.g., image complexity).

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

• blender_execA
  4.8/5.0
  Behavior5/5

  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. It thoroughly describes execution rules (e.g., script structure, API preferences, termination requirements, error handling) and return format (JSON string with status and messages), adding significant value beyond what the input schema provides.

  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 appropriately sized and front-loaded, starting with the core purpose followed by structured rules and parameter details. Every sentence earns its place by providing essential information without redundancy, making it efficient and well-organized.

  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?

  Given the tool's complexity (executing scripts in an external environment), lack of annotations, and presence of an output schema (which covers return values), the description is complete. It addresses purpose, rules, parameters, and behavioral expectations, leaving no significant gaps for the agent.

  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 schema description coverage is 0%, so the description must compensate. It adds detailed meaning for the single parameter 'script', explaining it as 'Python code to execute in Blender' with access to 'bpy' and 'send_status()', which clarifies semantics not evident from the schema alone.

  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 specific action ('Execute a Python script inside Blender') and resource ('Blender'), distinguishing it from sibling tools like 'get_blender_scene' (which retrieves scene data) and 'image_to_3d_model' (which converts images). It explicitly mentions the verb 'execute' and the target environment 'Blender', avoiding tautology.

  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 this tool (to run Python scripts in Blender) but does not explicitly mention when not to use it or name alternatives. The 'IMPORTANT RULES' section implies usage by setting prerequisites, but it lacks explicit exclusions or comparisons to sibling tools.

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