Server Quality Checklist

Tool Scores

• perf_validateA
  4.1/5.0
  Behavior4/5

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

  With no annotations, the description discloses behaviors: it fixes specific violations (malformed enums, truncated arrays, etc.) and returns valid output or rejection. This is sufficient for a validation tool, though it could mention side effects or permissions.

  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?

  Two concise sentences front-load the purpose and list key behaviors. Every sentence adds value without extraneous information.

  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 three parameters and lack of annotations, the description fully covers the tool's function, repair capabilities, and output, making it complete for an agent to invoke correctly.

  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%, so baseline is 3. The description does not add information beyond the schema's parameter descriptions, which already define content, target_schema, and repair_mode adequately.

  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 validates and auto-repairs LLM-generated JSON against a schema, using specific verbs and resources. It differentiates from siblings like perf_chat and perf_correct by focusing on validation and repair.

  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 for validating and repairing JSON, but does not provide explicit guidance on when to use it versus alternatives like perf_correct or perf_verify, nor does it state when not to use it.

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

• perf_correctA
  4.2/5.0
  Behavior4/5

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

  With no annotations, the description carries the full burden. It discloses that the tool classifies error types, applies correction, returns confidence scores, and rejects unfixable outputs. It also explains the behavior of the 'correction_budget' parameter (fast vs thorough). This provides good insight into the tool's actions and outcomes.

  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 three sentences with no redundant information. It front-loads the primary purpose, then provides usage guidance, and ends with output behavior. Every sentence adds value, making it 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 4 parameters, no output schema, and no annotations, the description covers the essential context: purpose, when to use, and return behavior (corrected output with confidence or rejection). It lacks details on error handling or side effects, but for a correction tool with a simple interface, it is adequately 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 coverage is 100% with each parameter described clearly (e.g., 'content' as LLM output, 'original_prompt' for drift detection, 'target_schema' for combined correction, 'correction_budget' with enum values and timing). The tool description does not add additional meaning beyond the schema, so the baseline score of 3 is appropriate.

  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 it is a general-purpose correction tool that classifies error types and applies specialized correction. It distinguishes from siblings by explicitly indicating when to use it (when unsure of specific tool or multiple error types), making its purpose and differentiation clear.

  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 provides explicit guidance on when to use this tool ('when unsure which specific tool to apply or when output has multiple error types'). It implies when not to use (when a specific tool is known) but does not explicitly exclude other scenarios, which is sufficient for clarity.

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

• perf_chatA
  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 the full responsibility. It discloses automatic model selection, retries, fallbacks, streaming, cost savings, and OpenAI-compatible format. It does not detail specific error handling or rate limits, but overall provides good behavioral 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 concise at three sentences, front-loaded with the main purpose, and every sentence provides necessary information without redundancy.

  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 no output schema, the description mentions 'OpenAI-compatible format', which implies a standard response structure. It covers key behaviors like streaming and cost savings. Could explicitly state the return format, but is sufficient for most agents.

  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?

  Schema description coverage is 100% with all 5 parameters described. The description adds value by explaining automatic model selection, retries, fallbacks, and cost savings, which are not captured in the schema alone. It complements the parameter descriptions effectively.

  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 routes LLM requests to the optimal model automatically, selecting from 20+ models based on task complexity and cost. It distinguishes from direct API calls to OpenAI/Anthropic and from sibling tools (perf_correct, perf_validate, perf_verify).

  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 advises using this tool instead of calling OpenAI or Anthropic directly, indicating a clear replacement use case. It does not explicitly state when not to use or compare with siblings, but the context is clear for an automated routing tool.

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

• perf_verifyA
  4.4/5.0
  Behavior4/5

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

  No annotations are provided, so the description carries full responsibility. It discloses multi-channel verification (web search, NLI, cross-reference) and states it returns corrected text with structured diff. It doesn't mention potential downsides like latency but provides adequate transparency for a non-destructive tool.

  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 and front-loaded with the core purpose. Every sentence adds meaningful information, with no unnecessary words or repetition.

  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 complexity (4 parameters, no output schema, no annotations), the description covers purpose, usage, verification method, and parameter tips. It lacks detailed return format information, but the mention of 'structured diff' provides some guidance.

  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?

  All parameters have schema descriptions (100% coverage). The description adds value by explaining the rationale for source_context ('enables cross-reference verification') and highlighting sensitivity levels. It provides context beyond the schema.

  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: detecting and repairing hallucinations in LLM-generated text. It uses specific verbs ('detect and repair') and resources, and distinguishes itself from siblings by mentioning multi-channel verification methods.

  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 advises when to use the tool ('before presenting AI content to users or writing to databases') and recommends providing source_context for best accuracy. It lacks explicit exclusions or comparisons to siblings, but 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.