Server Quality Checklist

Tool Scores

• Behavior2/5

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

  With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'analyze and transcribe' but fails to describe key traits such as processing time, error handling, output format, or any limitations (e.g., file size, supported audio formats). This leaves significant gaps for a tool that performs AI-based analysis.

  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 directly states the tool's function without unnecessary words. It is appropriately sized and front-loaded, making it easy 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 complexity of AI-based audio analysis, no annotations, and no output schema, the description is incomplete. It lacks details on behavioral aspects, output structure, and usage context, which are critical for an agent to effectively invoke this tool without trial and error.

  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 description coverage is 100%, with clear descriptions for all parameters (filepath, modelname, prompt). The description adds no additional semantic context beyond what the schema provides, such as examples or constraints, so it meets the baseline for high schema coverage without compensating value.

  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 verb 'analyze and transcribe' and the resource 'audio' with the technology 'Google Gemini AI', making the purpose evident. However, it doesn't explicitly differentiate from sibling tools like 'image_recognition' or 'video_recognition' beyond the audio focus, which prevents a perfect score.

  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 like sibling tools or other audio processing methods. It lacks context about use cases, prerequisites, or exclusions, leaving the agent to infer usage from the name alone.

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

• 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. While 'analyze and describe' implies a read-only operation, it doesn't specify whether this requires API keys, has rate limits, handles errors, or what the output format looks like. For a tool with no annotations and no output schema, this leaves significant behavioral gaps.

  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 - a single sentence that directly states the tool's purpose without any unnecessary words. It's front-loaded with the core functionality and uses efficient language. Every word earns its place in this minimal description.

  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 that there are no annotations, no output schema, and this is an AI analysis tool with potential behavioral complexities, the description is insufficiently complete. It doesn't explain what kind of analysis or description will be returned, doesn't mention authentication requirements for Google Gemini AI, and provides no context about limitations or error handling.

  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 schema already documents all three parameters (filepath, modelname, prompt) with clear descriptions. The tool description adds no additional parameter semantics beyond what's in the schema. According to the rules, when schema coverage is high (>80%), the baseline score 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 tool's purpose: 'Analyze and describe images using Google Gemini AI'. It specifies the verb ('analyze and describe'), resource ('images'), and technology ('Google Gemini AI'), making the purpose unambiguous. However, it doesn't explicitly differentiate from sibling tools like audio_recognition or video_recognition, which would require mentioning it's specifically for images.

  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 (audio_recognition, video_recognition) or any context for choosing this specific image analysis tool over others. There's no information about prerequisites, limitations, or typical use cases.

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

• Behavior2/5

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

  With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool analyzes and describes videos but doesn't mention critical behavioral aspects like rate limits, authentication requirements, file size limits, supported video formats, processing time, or error handling. The description is too vague about what 'analyze and describe' entails operationally.

  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 states the core functionality without unnecessary words. It's appropriately sized and front-loaded with the essential information, 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 complexity of video analysis (which typically involves format handling, processing time, and potential errors), no annotations, and no output schema, the description is insufficient. It doesn't explain what the tool returns, how to interpret results, or any operational constraints, leaving significant gaps for an AI agent to use it effectively.

  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 schema already documents all three parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema, such as explaining how the prompt interacts with video analysis or model selection trade-offs. Baseline 3 is appropriate when schema does the heavy lifting.

  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 analyzing and describing videos using Google Gemini AI, which is specific (verb+resource) and distinguishes it from sibling tools like audio_recognition and image_recognition. However, it doesn't explicitly mention video-specific capabilities beyond the name, leaving some ambiguity about whether it handles all video formats or specific features.

  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 its siblings (audio_recognition, image_recognition). It doesn't mention prerequisites, limitations, or alternative scenarios, leaving the agent to infer usage based on tool names alone.

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