Server Quality Checklist

Tool Scores

• list_modelsB
  3.1/5.0
  Behavior2/5

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

  With no annotations, the description must carry the full burden. It mentions visibility depends on the API key, but lacks details on whether the operation is read-only, destructive, rate-limited, or paginated. This is insufficient transparency.

  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?

  Single sentence with no redundant words. It efficiently conveys the core purpose without extraneous information.

  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 no output schema and two parameters, the description is too minimal. It omits expected return format, pagination behavior, error handling, and how filters affect results. A more complete description is needed for reliable agent invocation.

  Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

  Parameters2/5

  Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

  Schema description coverage is 50% (only 'filter' has a description). The description adds no meaning beyond the schema—it does not address parameters like 'limit' (pagination) or how 'filter' works. It fails to compensate for the schema gaps.

  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 verb ('list') and the resource ('models') with a specific scope ('visible to the configured NVIDIA API key'). It distinguishes the tool from siblings like 'chat_completion' and 'generate_image'.

  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?

  No usage guidelines are provided. The description does not indicate when to use this tool vs. alternatives (e.g., how it differs from 'check_nvidia_setup'). No exclusions or context for selection are given.

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

• chat_completionB
  3.3/5.0
  Behavior3/5

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

  No annotations provided. Description mentions 'non-streaming' but does not disclose side effects, authentication, rate limits, or error behavior. Minimal but not misleading.

  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?

  Single sentence, front-loaded with purpose. No wasted words, but could include brief parameter context without becoming verbose.

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

  Completeness3/5

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

  No output schema, so return value unclear. Sibling tools are listed but not differentiated. Description is adequate for a simple tool but lacks depth.

  Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

  Parameters2/5

  Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

  Schema coverage is low (33%). Description does not explain key parameters like max_tokens, temperature, include_usage, or model beyond their default values and constraints.

  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 verb 'run', the resource 'non-streaming chat completion', and the API provider. It distinguishes from siblings like generate_image and list_models.

  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?

  No guidance on when to use this tool versus alternatives (e.g., streaming, other API calls). No mention of prerequisites or context.

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

• generate_imageB
  3.4/5.0
  Behavior3/5

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

  The description discloses one key behavioral trait: 'Defaults to dry_run=true to avoid accidental credit usage.' This is valuable as it indicates the tool has a safety mechanism. However, with no annotations provided, the description carries the full burden for behavioral transparency, and it omits other important aspects such as required authentication, rate limits, or error handling.

  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 with two sentences, no redundant information. The first sentence establishes the purpose, and the second provides a critical behavioral note. It is front-loaded and earns its place without excess.

  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 tool's complexity (7 parameters, no output schema, no annotations), the description is incomplete. It does not explain the meaning of most parameters, the format of the generated image, error scenarios, or usage context. The dry_run note is helpful but insufficient for full understanding.

  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 low (29%). The description adds meaning to the dry_run parameter by explaining its default and purpose, which is not in the schema. It also provides context about the API being 'OpenAI-compatible.' However, it does not elaborate on other parameters like n, size, model, or force, leaving gaps in 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: 'Generate an image through NVIDIA's OpenAI-compatible image generation API.' It uses a specific verb and resource, and the title 'NVIDIA Image Generation' reinforces this. It distinguishes from sibling tools (chat_completion, check_nvidia_setup, list_models) which have different functions.

  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 lacks guidance on when to use this tool versus alternatives. It mentions the dry_run default to prevent accidental credit usage, but does not provide context for appropriate use cases or when not to use it (e.g., for simple text or other types of generation). No comparison with sibling tools is given.

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

• check_nvidia_setupA
  4/5.0
  Behavior3/5

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

  With no annotations, the description carries full burden. It states the tool checks for an API key and reveals URLs/output directory, but does not disclose if any state changes occur or if it requires network access. The behavior is simple but could be more explicit.

  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?

  A single, well-structured sentence conveys the tool's purpose efficiently without superfluous words. Perfect conciseness.

  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 (one optional boolean parameter, no output schema), the description is largely complete. It states what is checked, though it could mention that no output is returned or what 'sees an API key' means in practice.

  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 coverage is 100% as the only parameter (probe) has a description. The tool description does not add additional meaning beyond what the schema provides, so baseline 3 is appropriate.

  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 checks for API key presence and identifies base URLs and output directory. This is a distinct diagnostic purpose from sibling tools like chat_completion or generate_image, which are execution-oriented.

  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 implies usage for verifying configuration before running other tools, but it does not explicitly state when not to use it or contrast with alternatives. The context is clear but lacks exclusion guidance.

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