Server Quality Checklist

Tool Scores

• chatC
  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 the full burden of behavioral disclosure but offers minimal information. It mentions 'conversation' but doesn't describe response format, rate limits, authentication needs, conversation state management, or potential side effects. For a chat tool with zero annotation coverage, 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 at just 5 words ('Have a conversation with LibreModel (Gigi)'). It's front-loaded with the core purpose and contains no wasted words or unnecessary elaboration.

  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 chat tool with 6 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what the tool returns, how conversations are structured, whether there's session management, or any behavioral characteristics. The agent would need to guess about important operational aspects.

  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 input schema has 100% description coverage, so all parameters are documented in the schema. The tool description adds no parameter-specific information beyond what's in the schema. According to the 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 tool's purpose as 'Have a conversation with LibreModel (Gigi)', which is a specific verb (chat/converse) with a resource (LibreModel/Gigi). However, it doesn't distinguish this tool from potential alternatives like 'quick_test' or 'health_check' that might also interact with the model, so it lacks sibling differentiation.

  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 sibling tools 'health_check' or 'quick_test'. There's no mention of appropriate contexts, prerequisites, or exclusions. The agent must infer usage from the tool name alone.

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

• health_checkB
  3.2/5.0
  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 checks if the server is 'running and responsive,' which implies a read-only, non-destructive operation, but it doesn't specify details like response format, error handling, timeouts, or what 'responsive' entails. This leaves gaps in understanding the tool's behavior.

  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, clear sentence: 'Check if the llama-server is running and responsive.' It's front-loaded with the core purpose, has no unnecessary words, and efficiently conveys the essential information without waste.

  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 simplicity (0 parameters, no output schema, no annotations), the description is adequate but has gaps. It explains the basic purpose but lacks details on behavioral aspects like what the check entails or output expectations. For a health check tool, more context on response behavior would improve completeness, but it meets the minimum viable threshold.

  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 no parameter documentation is needed. The description doesn't add parameter details, which is appropriate here. A baseline of 4 is applied since the schema fully covers the lack of parameters, and the description doesn't introduce confusion.

  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: 'Check if the llama-server is running and responsive.' It specifies the verb ('Check') and resource ('llama-server'), making it understandable. However, it doesn't explicitly distinguish this from sibling tools like 'chat' or 'quick_test', which might also involve server interaction, so it falls short of 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. It doesn't mention scenarios like pre-operation verification, troubleshooting, or how it differs from siblings such as 'chat' or 'quick_test'. Without this context, users must infer usage based on the tool name alone.

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

• quick_testB
  3.2/5.0
  Behavior2/5

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

  With no annotations, the description carries full burden but only states it tests responsiveness. It doesn't disclose what 'responding' entails (e.g., latency expectations, success criteria), whether it's read-only or has side effects, or what happens with different test types. This leaves significant behavioral gaps for a tool with multiple test options.

  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 immediately states the tool's purpose. Every word earns its place with no redundancy or unnecessary elaboration, making it appropriately sized and front-loaded.

  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 no annotations, no output schema, and multiple test types, the description is insufficient. It doesn't explain what different test types do, what output to expect, or how to interpret results. The context signals show complexity (enum parameter) that isn't addressed, leaving the agent under-informed.

  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 coverage is 100%, with the parameter 'test_type' fully documented in the schema (enum values, default). The description adds no parameter-specific information beyond implying all types test responsiveness. Since the schema does the heavy lifting, the baseline 3 is appropriate despite the description's minimal contribution.

  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 ('Run a quick test') and the target ('LibreModel'), specifying the purpose as checking responsiveness. It distinguishes from 'health_check' by focusing on functional testing rather than system status, but doesn't explicitly differentiate from 'chat' beyond the testing context.

  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 when needing to verify LibreModel's responsiveness, but provides no explicit guidance on when to choose this tool over 'health_check' or 'chat'. The 'quick' qualifier suggests it's for lightweight verification, but alternatives and exclusions aren't addressed.

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