Server Quality Checklist

Tool Scores

• v0_generate_uiA
  3.5/5.0
  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 states the output type but does not clarify whether the tool writes files, returns code, or has side effects. It lacks important details like permissions, rate limits, or what happens on invocation, creating ambiguity for an agent.

  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, consisting of two sentences that front-load the primary purpose. There is no wasteful or repetitive content; every sentence adds value.

  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?

  The description covers the tool's purpose and tech stack, but without an output schema, it should clarify what the tool returns or does with the generated components. The schema handles parameter details, but the overall operational context (e.g., response format, side effects, or integration behavior) is incomplete.

  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 schema provides 100% parameter coverage, so the baseline is 3. The description's mention of 'natural language descriptions' aligns with the 'prompt' parameter, but adds no additional meaning beyond what the schema already specifies. The schema's descriptions are sufficiently detailed for each parameter.

  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 a specific verb ('Generate', 'Creates') and resource ('UI components', 'React components with TypeScript and Tailwind CSS'). It also distinguishes itself from the sibling v0_generate_from_image by specifying 'based on natural language descriptions', which signals the input type.

  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 for text-based UI generation but does not explicitly state when to use this tool vs alternatives like v0_generate_from_image. There is no mention of exclusions or alternative tools, leaving the agent to infer usage from the description.

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

• v0_generate_from_imageA
  3.6/5.0
  Behavior2/5

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

  With no annotations provided, the description bears the full burden of behavioral disclosure. It only restates the action (analyzes and creates React components) without mentioning side effects, prerequisites, failure modes, or security implications of fetching an image URL. This is insufficient for a tool that performs an external network request.

  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 two concise sentences that front-load the core purpose and add no redundant words. Every sentence earns its place, making it highly efficient.

  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 low-complexity tool with a clear schema and no output schema, the description adequately conveys that it takes an image URL and produces React components. It lacks a few contextual hints (e.g., need for accessible URL), but overall it is sufficient for an agent to understand what happened.

  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 provides 100% coverage with descriptions for all three parameters, including the enum and format for model and imageUrl. The description adds no extra parameter meaning beyond the schema, so the baseline score of 3 applies.

  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 a specific verb and resource: 'Generate UI components from an image reference.' It distinguishes itself from the sibling 'v0_generate_ui' by explicitly referencing image input, making its scope obvious.

  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 usage context is implied by the description—use this tool when you have an image reference—but there is no explicit guidance on when to prefer it over alternatives like 'v0_generate_ui' or when not to use it. No exclusions or alternative tool references are provided.

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

• v0_chat_completeA
  3.7/5.0
  Behavior2/5

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

  With no annotations, the description carries the full burden. It only states the high-level purpose (conversation for UI refinement) but does not disclose behavioral details such as whether it is read-only, if it modifies resources, how it handles conversation history, or what the response format is. This is similar to the update_drive example where mutation details were omitted.

  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 two sentences, front-loaded with the primary purpose, and contains no filler. Every word contributes to understanding the tool's function.

  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?

  The schema covers parameters well, but the description is thin on return value and behavioral specifics. It lacks an output schema, and the description does not explain what the tool returns (e.g., a completion message), which would be helpful for an agent to invoke it correctly.

  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 provides full descriptions for all three parameters (model, stream, messages), so the description adds no additional parameter meaning. Baseline of 3 is appropriate since schema coverage is 100%.

  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 a specific verb+resource: 'Have a conversation with v0 for iterative UI development.' It distinguishes from sibling tools by emphasizing 'back-and-forth refinement of UI components,' which contrasts with the generation-focused tools like v0_generate_ui and v0_generate_from_image.

  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 gives clear context for use: iterative UI development and refinement. However, it does not explicitly mention alternatives or when not to use this tool, so it falls 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.

• v0_setup_checkA
  3.9/5.0
  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 states the tool validates API key and endpoint accessibility, implying a read-only connectivity test. However, it doesn't disclose return format, success/failure behavior, or whether any side effects occur.

  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 concise sentences with no filler. Front-loads the purpose and adds a clarifying detail.

  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?

  The description is adequate for a simple 0-parameter check tool, but without an output schema, it should ideally mention what the response includes (e.g., status or error details). This omission makes it slightly incomplete.

  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 zero parameters, so the base score is 4. The schema is empty with 100% coverage, and no parameter semantics are needed.

  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 'Check' and identifies the resource as 'v0 API configuration and connectivity'. It further clarifies it validates API key and endpoint accessibility, which clearly distinguishes it from sibling generation tools.

  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?

  No explicit when-to-use or alternative guidance is provided. Usage is implied from the tool being a 'check' step, but it doesn't state when to run it versus the generation tools. This makes it adequate but not explicit.

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