Server Quality Checklist

Tool Scores

• generate_multimodal_responseC
  2.9/5.0
  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 only states the basic action ('generate response') without detailing behavioral traits like rate limits, authentication needs, error handling, or what happens with invalid inputs. For a complex tool with 8 parameters, this lack of context is a significant gap, though it doesn't contradict any 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 appropriately sized and front-loaded, starting with the core purpose followed by parameter details in a structured format. Every sentence serves a purpose, with no wasted words, though the parameter explanations could be more concise. It efficiently conveys essential information without unnecessary elaboration.

  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?

  Given the tool's complexity (8 parameters, no annotations, but with an output schema), the description is moderately complete. It covers the purpose and parameters but lacks behavioral context and usage guidelines. The output schema handles return values, so the description doesn't need to explain those, but it should provide more operational details to fully guide an AI agent.

  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 lists all 8 parameters with brief explanations (e.g., 'Model name to use', 'Text prompt'), adding meaning beyond the input schema, which has 0% description coverage. However, the explanations are minimal and don't cover details like format constraints or examples. With low schema coverage, this partially compensates but doesn't fully address the complexity, warranting a baseline score.

  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 response from multimodal model.' This specifies the verb ('generate response') and resource ('multimodal model'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'list_available_providers' or 'validate_multimodal_request', which serve different purposes (listing vs. validation vs. generation).

  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 doesn't mention sibling tools or any context for choosing this tool over others, such as for generating outputs versus validating requests. Without such guidance, an AI agent might struggle to select the appropriate tool in a given scenario.

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

• list_available_providersB
  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 states the tool returns a JSON string of providers and models, which is useful, but doesn't cover other behavioral traits like rate limits, authentication needs, error handling, or whether it's a read-only operation. The description adds some value but leaves significant gaps for a tool with no annotation coverage.

  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: one stating the purpose and one specifying the return format. It's front-loaded with the main action. However, the formatting includes extra indentation and a blank line, which slightly detracts from structure but doesn't impact clarity.

  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?

  Given the tool has 0 parameters, 100% schema coverage, and an output schema exists, the description is moderately complete. It explains what the tool does and the return format, but with no annotations, it should ideally cover more behavioral aspects like safety or performance. The output schema reduces the need to detail return values, but the description could better address gaps from missing annotations.

  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 0 parameters with 100% coverage, so the schema fully documents the lack of inputs. The description doesn't need to add parameter details, and it correctly avoids discussing parameters. This meets the baseline for 0 parameters, as the description focuses on output without redundancy.

  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 with a specific verb ('List') and resource ('available model providers and their configurations'). It distinguishes from sibling tools like 'generate_multimodal_response' and 'validate_multimodal_request' by focusing on listing rather than generating or validating. However, it doesn't explicitly differentiate itself from potential alternative listing tools beyond the scope of providers.

  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 doesn't mention prerequisites, context for usage, or compare it to sibling tools. The only implied usage is to retrieve provider information, but this is basic and lacks explicit when/when-not instructions.

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

• validate_multimodal_requestB
  3.1/5.0
  Behavior2/5

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

  With no annotations provided, the description carries full burden for behavioral disclosure. It only states the basic validation function without describing what 'supported' means (e.g., capability checks, rate limits, authentication requirements, error conditions, or what happens when validation fails). This leaves significant behavioral gaps for a tool that likely interfaces with external services.

  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 extremely concise and well-structured: a clear purpose statement followed by parameter and return value documentation in a standard format. Every sentence earns its place with no redundant information.

  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?

  Given the tool's moderate complexity (4 parameters, validation logic) and the presence of an output schema (which handles return values), the description is minimally adequate. However, with no annotations and 0% schema description coverage, it should provide more behavioral context about what validation entails and error scenarios.

  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 lists all 4 parameters with brief explanations, adding meaning beyond the bare schema. However, it doesn't provide format details (e.g., model name patterns), constraints (e.g., valid ranges for counts), or how parameters interact (e.g., if provider affects model validation).

  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 as validating if a multimodal request is supported, which is a specific verb (validate) applied to a specific resource (multimodal request). However, it doesn't explicitly differentiate from sibling tools like 'generate_multimodal_response' or 'list_available_providers' beyond the validation focus.

  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 doesn't mention prerequisites, typical use cases, or relationships to sibling tools like 'generate_multimodal_response' (which might require validation first) or 'list_available_providers' (which might help select a provider).

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