Server Quality Checklist

Tool Scores

• aichat_create_conversation_v2C
  2.9/5.0
  Behavior2/5

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

  No annotations are present, so the description carries the full burden of behavioral disclosure. It only states that conversations can be created or managed; it does not mention side effects, persistence behavior, authorization requirements, async semantics, or what happens to existing conversations. It adds very little beyond the tool's name.

  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 a single short sentence with no wasted words and is easy to scan. However, for a tool with 22 optional parameters and five possible actions, the extreme brevity is more under-specification than elegant conciseness.

  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?

  This is a complex multi-action tool, yet the description gives no indication of how the action parameter routes behavior or which parameter combinations matter. The output schema and parameter descriptions help, but an agent still lacks essential usage context for selecting among chat, retrieve, retrieve_batch, update, and delete.

  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 itself adds no parameter-level meaning, but the input schema documents all 22 parameters with descriptions and defaults, giving 100% schema coverage. Therefore the schema carries the parameter-semantics burden, and the baseline of 3 is appropriate.

  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 names the resource ('conversations') and a clear set of actions ('create/manage'), and it ties the tool to the 'v2 endpoint'. It does not, however, differentiate this from the sibling aichat_create_conversation beyond the version label, so it stops short of full clarity.

  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?

  There is no guidance on when to use this tool instead of aichat_create_conversation, aichat_list_models, or aichat_get_usage_guide. The phrase 'v2 endpoint' hints at a version preference but provides no concrete selection criteria or exclusions.

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

• aichat_create_conversationA
  3.9/5.0
  Behavior4/5

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

  With no annotations provided, the description carries the behavioral disclosure burden. It clearly states the core behavior: it creates/continues a conversation, sends a question to an AI model, and returns a JSON response with conversation ID and answer. It does not mention persistence or stateful nuances, but the schema already explains stateful behavior, so this is a minor 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 well-organized with a clear opening action, a 'Use this when' section, and a Returns section. It is reasonably compact and front-loaded, though the model-support list is partially redundant with the schema enum.

  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 a 6-parameter schema with 100% coverage and an output schema, the description provides enough context to invoke the tool correctly: it explains the main action, continuation via conversation_id, and the response shape. The only notable completeness gap is the lack of distinction vs aichat_create_conversation_v2.

  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 structured documentation already fully explains every parameter. The description adds modest value by tying question and conversation_id to concrete use cases and highlighting model breadth, but it does not meaningfully expand on parameter meaning.

  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 identifies the operation: 'Create an AI conversation using the AiChat API' and 'Sends a question to the specified AI model and returns the generated answer.' It is distinct from aichat_list_models and aichat_get_usage_guide, but it does not differentiate from the sibling aichat_create_conversation_v2.

  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 'Use this when' bullets provide explicit use cases: asking a question, continuing an existing conversation with conversation_id, and requesting specific model families like DeepSeek or Grok. However, it gives no exclusions and does not mention when the sibling v2 tool should be used instead.

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

• aichat_list_modelsA
  4.1/5.0
  Behavior3/5

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

  No annotations provided. Description indicates it returns a formatted list with descriptions, but doesn't discuss potential behaviors like rate limits or pagination. Adequate for a simple read-only operation.

  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?

  Concise, front-loaded with the action ('List all available AI models'), and each sentence adds value. No wasted words.

  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 zero parameters and a simple purpose, the description covers all needed information. Output schema exists, so return values are handled. Context signals indicate high schema coverage and no nested objects.

  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?

  No parameters exist, so baseline is 4. Description does not need to add parameter semantics.

  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 'List all available AI models for the AiChat API' and provides specific examples (GPT-4/5, o-series, etc.). It distinguishes from sibling tools (create_conversation, usage_guide) by focusing on listing models.

  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 guidance on when to use this tool versus alternatives. However, with zero parameters and a straightforward purpose, usage is implied (e.g., before creating a conversation).

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

• aichat_get_usage_guideA
  4.3/5.0
  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 returns a 'Complete usage guide' but does not explicitly declare idempotence or lack of side effects. For a simple read-only tool, this is adequate 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?

  The description is concise (3 short sentences), front-loads the purpose, and every sentence adds value. There is no waste, though the first two sentences are slightly redundant; still, the overall structure is excellent.

  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 has no parameters and an output schema exists (per context), the description is complete. It clearly explains the return value ('Complete usage guide for AiChat tools'), which is sufficient for an agent to understand the tool's function.

  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 parameter semantics are not applicable. The description adds value by explaining what the tool returns (usage guide), which justifies the baseline score of 4 for tools with no parameters.

  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: 'Get a comprehensive guide for using the AiChat tools.' It distinguishes itself from sibling tools like aichat_create_conversation and aichat_list_models by focusing on documentation rather than actions.

  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 obtaining guidance ('Provides detailed information on how to use the AiChat tools effectively'), but does not explicitly state when to use this versus alternatives or provide exclusions. Nonetheless, the context is clear.

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