Server Quality Checklist

Tool Scores

• list_gemini_modelsA
  4.3/5.0
  Behavior3/5

  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 mentions 'capabilities and limitations' which hints at behavioral traits, but doesn't specify whether this is a read-only operation, whether it requires authentication, what format the output takes, or if there are rate limits. The description adds some context but leaves important behavioral aspects unspecified.

  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 perfectly structured with a clear opening statement followed by three specific bullet points. Every sentence earns its place by providing distinct value: the first establishes purpose, the bullets provide concrete usage scenarios. No wasted words or redundancy.

  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 zero parameters and no output schema, the description provides adequate purpose and usage guidance. However, for a tool with no annotations, it should ideally mention whether this is a read operation, what the output format looks like (list of models with metadata), and any authentication requirements. The current description is complete enough for basic understanding but lacks operational details.

  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 with 100% schema description coverage, so the schema already fully documents the parameter situation. The description appropriately doesn't discuss parameters since none exist, maintaining focus on the tool's purpose and usage. This earns a baseline 4 for zero-parameter tools.

  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 specific verb ('List') and resource ('available Gemini AI models and their capabilities'). It distinguishes from sibling tools count_gemini_tokens (token counting) and query_gemini (model querying) by focusing on model discovery and capability assessment.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines5/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  The description explicitly provides three bullet points detailing when to use this tool: to discover models, understand capabilities/limitations, and choose appropriate models for tasks. This gives clear guidance on its intended context and distinguishes it from alternatives like query_gemini which would be used after model selection.

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

• count_gemini_tokensA
  4.4/5.0
  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 describes the tool's purpose and use cases but lacks behavioral details such as rate limits, error handling, or whether it's a read-only operation (though implied by 'count'). It adds value by explaining the practical applications but misses some 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 front-loaded with the core purpose in the first sentence, followed by a bulleted list of use cases that are directly relevant and efficient. Every sentence earns its place without redundancy, making it highly concise and well-structured.

  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 low complexity (single parameter, no output schema, no annotations), the description is mostly complete. It covers purpose, usage, and parameter context, but lacks details on output format or error handling, which would be helpful for full completeness. It's adequate but has minor gaps.

  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 100% description coverage, so the baseline is 3. The description adds value by clarifying that the text is for 'the configured Gemini model,' which provides context beyond the schema's generic 'text to count tokens for.' However, it doesn't detail tokenization specifics or model dependencies, keeping it at 4.

  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 specific action ('Count the number of tokens') and resource ('text string for the configured Gemini model'), distinguishing it from siblings like list_gemini_models (listing models) and query_gemini (making queries). It avoids tautology by explaining what counting tokens means rather than just restating the name.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines5/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  The description explicitly provides three use cases (estimating costs, ensuring context limits, optimizing length), which clearly indicate when to use this tool. It implicitly distinguishes from query_gemini by focusing on pre-query analysis rather than actual querying, though it doesn't explicitly name alternatives.

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

• query_geminiA
  4.4/5.0
  Behavior4/5

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

  With no annotations provided, the description carries full burden and does well by disclosing key behavioral traits: supports conversation history for multi-turn interactions, streaming enabled by default for better responsiveness. It doesn't mention rate limits, authentication needs, or error behaviors, but provides substantial 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 well-structured and front-loaded with the core purpose, followed by usage guidelines and behavioral details. Every sentence earns its place - no redundant information, and the bulleted list efficiently communicates usage scenarios without verbosity.

  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 query tool with no annotations and no output schema, the description provides good context about when to use it and behavioral characteristics. It could be more complete by mentioning response format, error handling, or model selection options, but covers the essential operational aspects well given the complexity.

  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 the baseline is 3. The description adds minimal parameter semantics beyond the schema - it mentions 'conversation history for multi-turn interactions' which relates to the history parameter, but doesn't provide additional context about prompt construction or streaming implications beyond what's in the schema descriptions.

  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 as 'Query Google's Gemini AI models for text generation, reasoning, and analysis tasks' - a specific verb+resource combination. It distinguishes from sibling tools (count_gemini_tokens, list_gemini_models) by focusing on querying rather than token counting or model listing.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines5/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  The description provides explicit usage scenarios in a bulleted list: 'Get a second opinion or alternative perspective', 'Leverage Gemini's specific capabilities', 'Generate content using a different AI model', and 'Compare responses between AI models'. These give clear guidance on when to use this tool versus alternatives.

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