Server Quality Checklist

Tool Scores

• ask_geminiC
  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 full burden for behavioral disclosure. 'Ask Google Gemini AI a question' implies a read-only query operation, but provides no information about rate limits, authentication requirements, response formats, error handling, or any behavioral characteristics. The description is minimal and lacks essential operational context.

  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 at just 5 words, front-loading the essential information with zero wasted words. It efficiently communicates the core purpose without unnecessary elaboration. Every word earns its place in this minimal but complete statement of function.

  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?

  For a tool with 3 parameters, no annotations, and no output schema, the description is insufficiently complete. While concise, it lacks critical context about behavioral characteristics, output format, error conditions, and differentiation from sibling tools. The agent would need to rely heavily on schema information alone, which is inadequate for proper tool selection and 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?

  With 100% schema description coverage, the input schema already documents all three parameters thoroughly. The description adds no additional parameter information beyond what's in the schema. According to the scoring rules, when schema_description_coverage is high (>80%), the baseline is 3 even with no param info in the description.

  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 action ('Ask') and target resource ('Google Gemini AI'), making the purpose immediately understandable. It distinguishes from 'ask_openai' by specifying the AI provider, though it doesn't explicitly mention the sibling differentiation. The description is specific enough to understand what the tool does without being tautological.

  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 the 'ask_openai' sibling tool. There's no mention of differences in capabilities, cost, performance, or appropriate use cases between Gemini and OpenAI. The agent receives no help in choosing between these two similar tools beyond the provider name.

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

• server_infoB
  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 retrieves information ('Get'), implying a read-only operation, but doesn't disclose any behavioral traits such as authentication requirements, rate limits, error conditions, or what specific data is returned. This is a significant gap for a tool with zero 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.

  Conciseness5/5

  Is the description appropriately sized, front-loaded, and free of redundancy?

  The description is a single, efficient sentence with zero waste. It's appropriately sized and front-loaded, directly stating the tool's purpose without unnecessary elaboration, 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 lack of annotations and output schema, the description is incomplete. It doesn't explain what 'server status and configuration' entails (e.g., uptime, version, settings), how the data is returned, or any prerequisites. For a tool with no structured data support, this leaves critical gaps in understanding its behavior and output.

  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 0 parameters, and the input schema has 100% description coverage (though empty). The description doesn't need to add parameter semantics, so it meets the baseline of 4 for tools with no parameters, as there's nothing to compensate for.

  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 ('Get') and resource ('server status and configuration'). It distinguishes what information is retrieved, though it doesn't explicitly differentiate from sibling tools like 'ask_gemini' or 'ask_openai', which appear to serve different purposes (querying AI models vs. server diagnostics).

  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 is provided on when to use this tool versus alternatives. The description implies usage for checking server state but doesn't specify scenarios (e.g., troubleshooting, monitoring) or exclusions, leaving the agent to infer context without explicit direction.

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

• ask_openaiB
  3.2/5.0
  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 states the basic action but doesn't mention rate limits, authentication requirements, cost implications, response format, or error handling. For a tool that likely involves API calls with potential constraints, this leaves significant behavioral aspects undocumented.

  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 a single, efficient sentence that communicates the core purpose without unnecessary words. It's appropriately sized for a straightforward tool and front-loads the essential information. Every word earns its place in this minimal but complete statement.

  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?

  For a tool with 3 parameters, no annotations, and no output schema, the description provides only basic purpose information. It doesn't address behavioral aspects, return values, error conditions, or usage constraints. Given the complexity of interacting with external AI models and the lack of structured metadata, the description is insufficiently complete.

  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%, providing complete parameter documentation. The description doesn't add any parameter-specific information beyond what's in the schema. According to scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no param info in the description, which applies here.

  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 action ('ask') and target resource ('OpenAI GPT models'), making the purpose immediately understandable. It distinguishes from the sibling 'ask_gemini' by specifying OpenAI rather than Google's model. However, it doesn't specify what type of question or interaction this enables (e.g., chat completion, text generation), leaving some ambiguity about the exact operation.

  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 context (when you need to query OpenAI models) but doesn't explicitly state when to use this versus the 'ask_gemini' sibling tool. There's no guidance on prerequisites, limitations, or alternative scenarios. The implied differentiation is present but not articulated clearly enough for optimal agent decision-making.

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