Server Quality Checklist

Tool Scores

• render_chartC
  2.9/5.0
  Behavior2/5

  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 renders and returns an image, but doesn't cover important aspects like performance characteristics (e.g., rendering time), error handling, rate limits, authentication requirements, or whether it's idempotent. For a tool that generates images, this is a significant gap.

  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 concise with two sentences that efficiently convey the core functionality. The first sentence states the purpose and output, while the second lists supported chart types. There's no wasted text, though it could be slightly more structured for clarity.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness2/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  Given the complexity of a chart rendering tool with 7 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain the return format beyond 'inline image' (e.g., base64 encoding, MIME type), error conditions, or how to interpret the Chart.js specification. For a tool with this level of complexity, more contextual information is needed.

  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 description adds minimal parameter semantics beyond the schema. It mentions 'Chart.js JSON specification' and lists supported chart types (which are already in the schema's enum), but doesn't explain the relationship between parameters (e.g., how labels correspond to datasets) or provide examples. With 57% schema description coverage, the description doesn't adequately compensate for the gaps in parameter documentation.

  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: 'Render a chart from a Chart.js JSON specification. Returns the chart as an inline image.' It specifies the verb ('render'), resource ('chart'), and output format ('inline image'), but doesn't explicitly differentiate from sibling tools like render_chart_ai or render_chart_url, which would require a 5.

  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 its siblings (render_chart_ai, render_chart_url). It mentions supported chart types but doesn't indicate any prerequisites, alternatives, or exclusion criteria. This leaves the agent without context for tool selection.

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

• render_chart_aiB
  3.1/5.0
  Behavior2/5

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

  No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the AI layer and API key requirement, which adds some context about functionality and access control. However, it lacks details on rate limits, error handling, response format (e.g., image data vs. URL), or any destructive effects, leaving significant gaps for a tool that generates charts.

  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 concise and front-loaded, with two sentences that efficiently convey the core functionality and requirements. Every sentence adds value: the first explains the tool's purpose, and the second provides essential context about the AI layer and API key. There's no wasted text, 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.

  Completeness2/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  Given the complexity of a chart generation tool with 5 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., image binary, URL, or error details), how to handle the AI layer's behavior, or any limitations beyond the API key requirement. This leaves the agent with insufficient information for reliable invocation.

  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 40%, meaning most parameters are not described in the schema. The description adds minimal value by implying that 'description' is natural language and 'data' is optional raw data, but it doesn't elaborate on the semantics of 'width', 'height', or 'format' beyond what the schema provides (e.g., default values and constraints). This partially compensates but doesn't fully address the coverage gap.

  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: 'Generate a chart from a natural language description and optional raw data.' It specifies the verb ('generate'), resource ('chart'), and method ('from natural language description and optional raw data'). However, it doesn't explicitly differentiate from sibling tools like 'render_chart' and 'render_chart_url', which likely have different approaches or outputs.

  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 provides some usage context by mentioning that it uses 'Chart-Output's AI layer' and requires 'a Pro or Business API key,' which implies when to use it based on subscription level. However, it doesn't explicitly state when to choose this tool over its siblings (e.g., 'render_chart' and 'render_chart_url') or any alternatives, leaving the agent to infer usage scenarios.

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

• render_chart_urlA
  4.2/5.0
  Behavior3/5

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

  No annotations are provided, so the description carries the full burden. It discloses that the tool returns a 'stable URL' from a CDN, which is useful behavioral context. However, it lacks details on permissions, rate limits, URL expiration, or error handling, which are important for a tool generating external resources.

  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 front-loaded with the core purpose in the first sentence, followed by usage guidelines and a note on inputs. Every sentence earns its place with no wasted words, making it highly efficient and well-structured.

  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 complexity (7 parameters, no annotations, no output schema), the description is reasonably complete for guiding usage. It clarifies the tool's purpose and when to use it, but lacks details on output format (e.g., URL structure, expiration) and error scenarios, which could be important for integration.

  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 0%, so the description must compensate. It adds value by noting 'Same inputs as render_chart,' implying parameters are consistent with a known sibling tool. However, it doesn't explain specific parameters like 'type' enums or 'datasets' structure, leaving significant gaps in parameter understanding.

  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: 'Render a chart and return a CDN URL instead of image bytes.' It specifies the action (render), resource (chart), and output format (CDN URL), and distinguishes it from sibling tools by noting it returns a URL rather than image bytes and referencing 'render_chart' as having the same inputs.

  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: 'Use this when you need a stable URL to embed in HTML, email, or pass to another tool.' It provides clear context for usage (embedding, email, passing to other tools) and distinguishes it from alternatives by implying 'render_chart' returns image bytes instead.

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