Server Quality Checklist

Tool Scores

• get_exampleA
  4/5.0
  Behavior3/5

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

  With no annotations, the description carries the burden of behavioral disclosure. It states the tool returns a full JSON spec and that the name comes from list_examples, but it does not mention potential errors, authentication requirements, or whether the operation is read-only. This is adequate for a basic fetch but lacks depth.

  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?

  Two sentences, front-loaded with the purpose, followed by concise usage guidance. Every word serves a purpose with no redundancy or filler.

  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 simple one-parameter tool with no output schema, the description sufficiently explains what is returned (full JSON spec) and how to use it (as a template, preserving track structure). It references list_examples for discovery, making the tool self-contained in context. Missing details like exact return format or error cases are minor given the tool's simplicity.

  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?

  The input schema covers the 'name' parameter completely with a clear description, and the tool description reinforces the source (list_examples) but adds little beyond the schema. Baseline of 3 is appropriate because the schema already documents the parameter well.

  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 fetches a full JSON spec for a named example, distinguishing it from list_examples (which lists examples) and other sibling tools. The verb 'Fetch' and resource 'full JSON spec of a named example' are specific and actionable.

  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 guidance on how to use the returned spec (as a starting template, with specific modifications like icons and labels) and implies the discovery flow via list_examples. It does not explicitly state when not to use this tool, but the context is sufficient for a simple fetch operation.

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

• list_iconsA
  4/5.0
  Behavior3/5

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

  With no annotations, the description carries the transparency burden. It adds useful context about the icons' line-style and consistent visual language, but it does not disclose behaviors like case-insensitivity, no-match handling, or limit enforcement beyond what the schema already states.

  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 concise at two sentences, front-loaded with the primary purpose, and includes an example and a generalizable insight about the icon set. Every sentence contributes value without redundancy.

  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 simple search tool with two optional parameters and no output schema, the description adequately covers purpose, result format (icon names), and stylistic consistency. It omits edge-case behavior like empty results or pagination, but these are minor for this 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 baseline is 3. The description adds example icon names, which clarifies the expected format, but does not significantly enhance the meaning of the parameters beyond the schema's own 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 searches the Tabler icon library by name substring, using a specific verb and resource. This distinguishes it from sibling tools like list_examples, validate_scene, and render_scene, which serve different purposes.

  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 that the tool is for finding icon names usable in scene specs, implying when to use it. However, it does not explicitly mention alternatives or when not to use it, stopping short of a 5.

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

• validate_sceneA
  4.7/5.0
  Behavior4/5

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

  With no annotations, the description carries the full burden. It clearly discloses that the tool does not render (side-effect-free validation), states the exact return shape, and reveals behavioral nuances like default orientation (portrait) and auto-flipping of layouts. It could go further by disclosing potential errors or side effects, but for a validation-only tool this is sufficient.

  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?

  Though the description is long, it is well-structured and front-loaded with the core purpose and return type, followed by actionable guidance. Every sentence earns its place by providing decision rules (track kinds, orientation, list_examples) or clarifying behavior. No fluff or tautology.

  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 tool is complex (nested schema, multiple track kinds), but the description covers all essential context: what the tool does, when to use it, how to choose track kinds, how to get canonical examples, and supported orientations. It also mentions the return shape, which is sufficient given no output schema. The guidance to call list_examples fills potential gaps in schema understanding.

  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 input schema has 100% description coverage, so the baseline is 3. The tool description adds value by explaining how to choose the track 'kind' based on content shape (flow, hub, comparison, etc.) and how orientation affects layout, which goes beyond the schema's field-level definitions. This extra guidance materially improves an agent's ability to construct a valid spec.

  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 opens with a specific verb+resource: 'Validate a scene spec against the schema without rendering.' It also defines the return shape (`Returns { ok, errors? }`) and explicitly distinguishes itself from the sibling `render_scene` by stating it is for checking structure before rendering, making its purpose unambiguous.

  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 guidance: 'Useful to check structure before render_scene.' It then gives detailed instructions on choosing track kinds based on content shape, including 'Do not reach for list-reveal unless...' and 'Use flow when...' for each relevant kind. It also instructs to 'Call list_examples first' and clarifies orientation options, fully covering usage alternatives.

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

• list_examplesA
  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 fully carries the behavior disclosure burden. It reveals the return structure (array of { name, kinds, orientation }), the data source (examples/), and dynamic layout behavior (auto-flip based on orientation), going far beyond a bare 'list' claim.

  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 longer than typical for a no-parameter list tool, but it is dense with useful guidance (return shape, cloning workflow, track-kind selection, orientations, auto-flip). It loses a point for slight redundancy, repeating 'ALWAYS call' and the call-first advice twice.

  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 low complexity, no parameters, and no output schema, the description is remarkably complete: it states what is listed, what each entry contains, where it comes from, how to use it, and both supported orientations. An agent has all needed context to select and call this tool correctly.

  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 takes zero parameters and the schema coverage is 100%, so there is no parameter ambiguity. The baseline for no-parameter tools is 4, and the description adds no unnecessary parameter details—correctly focusing on the tool's output and 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 uses a specific verb ('List') and resource ('canonical scene examples bundled with the kit'), immediately differentiating it from siblings like list_icons and get_example. It also clarifies the return shape and source directory, making the tool's purpose unmistakable.

  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 instructs to 'ALWAYS call this before authoring a new scene' and explains how to pick the closest example to clone. It provides detailed contextual guidance on selecting track kinds by content shape and covers orientation decisions, making it clear when and how to use this tool.

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

• render_sceneA
  4.8/5.0
  Behavior5/5

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

  With no annotations, the description carries full responsibility. It discloses return values, default resolution, fps, duration constraints, transparent background behavior, and auto-flipping layout, giving a thorough view of the tool's behavior.

  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 front-loaded with the core purpose and returns, followed by essential guidance on track kinds and orientation. It is lengthy but each sentence serves a purpose; however, it could be condensed and organized for even quicker consumption.

  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 complexity of the spec object and no output schema, the description covers return format, constraints, orientation choices, and references list_examples for canonical specs. It is complete for an agent to use the tool effectively.

  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 input schema already has 100% description coverage, so baseline is 3. The description adds value by explaining track-kind selection rules and orientation flipping, which are semantic nuances beyond the schema's field definitions.

  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 renders a scene spec to an MP4 (or .mov) and returns a structured result. It distinguishes itself from siblings like list_examples and validate_scene by focusing on the actual rendering output.

  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?

  Provides explicit, detailed guidance on when to use each track kind based on content shape, and directs agents to call list_examples first. It also covers orientation selection and how to ask the user or infer from context, making usage conditions very clear.

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