Server Quality Checklist

Tool Scores

• list_nodesA
  3.7/5.0
  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. It effectively discloses behavioral traits: it describes the return format for different filter_type values, error handling, and default behavior. However, it lacks details on rate limits, authentication needs, or side effects, which would be beneficial for a read operation.

  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 appropriately sized but not optimally structured. It front-loads the purpose, but includes extensive parameter explanations and examples that could be more concise. Every sentence adds value, but some redundancy exists (e.g., repeating filter_type details).

  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 no annotations, 0% schema coverage, and no output schema, the description provides good completeness. It explains parameters, return values, and error handling. However, it lacks context on prerequisites (e.g., API setup) or performance considerations, which would enhance completeness for a tool with 4 parameters.

  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 significant meaning beyond the schema: it explains filter_type values ('names', 'ids', 'properties'), default behavior, and how optional parameters (class_name, name, id) filter nodes. Examples further clarify usage, fully covering all 4 parameters.

  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: 'List MRML nodes via the Slicer Web Server API.' It specifies the verb ('List') and resource ('MRML nodes'), but does not differentiate from sibling tools like 'capture_screenshot' or 'execute_python_code', which are unrelated. The purpose is specific but lacks sibling comparison.

  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 explains parameters and examples but does not mention any sibling tools or contexts where this tool is preferred. Usage is implied through examples, but explicit guidelines are missing.

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

• capture_screenshotA
  4/5.0
  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. It effectively describes key behaviors: it's a read-only operation (capturing screenshots implies no mutation), provides real-time visual feedback, and returns content in MCP's standard format for display. It doesn't mention rate limits, authentication needs, or error conditions, but covers the core functionality well given the lack of annotations.

  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 (purpose, parameters, returns, examples). It's appropriately sized for a tool with 6 parameters, though some sentences like 'enabling AI to observe the GUI...' could be more concise. Overall, it's front-loaded with the core purpose and efficiently documents parameters without unnecessary fluff.

  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 (6 parameters, no output schema, no annotations), the description is quite complete. It covers purpose, detailed parameter usage, return format, and includes practical examples. The main gap is the lack of output schema, but the description compensates by specifying the return format. It could benefit from more behavioral context like error handling, but it's largely sufficient.

  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?

  Schema description coverage is 0%, so the description must fully compensate. It does this excellently by providing detailed parameter semantics: it explains each parameter's purpose, lists all valid options with clear enums (e.g., 'application', 'slice', '3d' for view_type), specifies dependencies (e.g., 'Only used when view_type="slice"'), and includes default values. This adds substantial meaning beyond the bare 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's purpose: 'Capture a screenshot from 3D Slicer's views.' It specifies the resource (3D Slicer's views) and verb (capture), but doesn't explicitly differentiate from sibling tools like 'execute_python_code' or 'list_nodes', which have completely different functions. The description is clear but lacks explicit sibling differentiation.

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

  Usage Guidelines3/5

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

  The description implies usage context through the phrase 'enabling AI to observe the GUI and make informed decisions in a complete REACT loop,' suggesting this is for visual feedback during AI interaction. However, it doesn't provide explicit guidance on when to use this tool versus alternatives (none of which are screenshot-related), nor does it mention any prerequisites or exclusions. Usage is implied rather than explicitly stated.

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

• execute_python_codeA
  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 delivers substantial behavioral information. It explains the execution environment (3D Slicer's Python environment), the execution mechanism (Python's exec() function), how to capture return values (assign to __execResult), and detailed success/failure response patterns including specific error types. The only gap is lack of information about permissions, rate limits, or side effects.

  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 (Parameters, Examples, Returns) but could be more front-loaded. The initial statement is clear, but some redundancy exists (e.g., explaining the code parameter twice). Most sentences earn their place by providing essential information, though minor trimming could improve conciseness.

  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 (arbitrary code execution), lack of annotations, and no output schema, the description provides comprehensive context. It covers execution mechanics, return value handling, success/failure scenarios with concrete examples, and error types. For a powerful tool with zero structured metadata, this description provides nearly everything an agent needs to use it correctly.

  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?

  With 0% schema description coverage for the single parameter, the description fully compensates by providing rich semantic context. It explains that 'code' is executable Python code, specifies it should work with exec(), provides concrete examples showing proper formatting, and explains the special __execResult variable for return values. This goes far beyond what the bare schema provides.

  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 Python code') and the target environment ('in 3D Slicer'), distinguishing it from siblings like capture_screenshot and list_nodes. It provides a precise verb+resource combination that leaves no ambiguity about the tool's function.

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

  Usage Guidelines3/5

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

  The description implies usage context through examples (creating models, getting node counts, calculations) but doesn't explicitly state when to use this tool versus alternatives. There's no guidance on prerequisites, limitations, or comparison with sibling tools, leaving the agent to infer appropriate use cases from the examples provided.

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