Server Quality Checklist

Tool Scores

• ask_grokB
  3.3/5.0
  Behavior2/5

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

  No annotations are provided, so the description bears full responsibility. It discloses that the tool supports system prompts and sampling parameters, but does not describe response format, rate limits, authentication needs, or any behavioral traits beyond the basic action.

  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 concise sentences with no wasted words. It front-loads the core purpose and efficiently lists supported parameters and additional guidance.

  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 6 parameters, no output schema, and no annotations, the description is adequate but incomplete. It covers default model and parameter types, but lacks details on output format, error handling, or behavioral constraints for a chat tool.

  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 baseline is 3. The description adds minimal extra meaning: it mentions the default model 'grok-4.3' and that system prompt is optional, but otherwise does not enhance understanding of temperature, max_tokens, or top_p beyond their schema descriptions.

  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 asks Grok a question and gets a response, and mentions default model and supported parameters. However, it does not differentiate from siblings like grok_consensus or grok_validate, which limits clarity on when to use this specific tool.

  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?

  Usage is implied: ask a question and get a response. It suggests using list_models for model options, but provides no explicit guidance on when to use this tool versus alternatives like grok_consensus, nor any exclusion criteria.

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

• generate_imageB
  3.4/5.0
  Behavior2/5

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

  No annotations provided, so the description must carry the behavioral disclosure burden. It mentions saving to local file and default model, but omits critical details like file overwrite behavior, supported formats, permissions, rate limits, or error handling.

  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 with no unnecessary words. The most important action ('generate an image... and save to a local file') is 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?

  No output schema and the description does not mention return value. Given the tool creates and saves a file, missing info on what is returned (e.g., saved path, success status) reduces completeness.

  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 covers 100% of parameters with clear descriptions. The description adds useful context about default model and linking to list_models, but does not provide significant additional meaning 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 it generates an image using Grok's Aurora model and saves to file. It distinguishes from sibling tools (ask_grok, grok_consensus, grok_validate, list_models) which serve different purposes.

  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 gives guidance on using the 'model' parameter to switch image models, but does not provide explicit when-to-use or when-not-to-use instructions relative to siblings.

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

• grok_consensusA
  3.9/5.0
  Behavior3/5

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

  The description discloses the iterative nature, default round count, and customizable rounds, but lacks details on costs (latency), model participation specifics, and limitations. With no annotations, the description carries full burden but falls short of full transparency.

  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 two sentences long, front-loads the core function and return type, and avoids redundancy. Every sentence provides essential information.

  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 complexity of a multi-round consensus protocol between two models, the description provides basic understanding but omits details on output structure, round mechanics, and use case scenarios. Adequate but not comprehensive.

  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 description adds value beyond the schema by clarifying default rounds (3-5) and explaining the trade-off for higher round counts (deeper analysis vs. latency). Both parameters are covered in schema, so this additional context is beneficial.

  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 runs an iterative Consensus Validation Protocol between two models, which distinguishes it from siblings like ask_grok (simple Q&A) and generate_image (image generation). It specifies the return type (structured final summary) and key parameters.

  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 the tool is for deep analysis requiring consensus but does not explicitly state when to use it over alternatives or provide exclusions. Siblings are named but no comparative guidance.

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

• list_modelsA
  4.1/5.0
  Behavior3/5

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

  No annotations provided, so description carries the burden. It discloses listing with IDs and capabilities and filtering, but does not mention permissions, rate limits, or side effects. For a read-only listing, this is adequate but not exceptional.

  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 sentences, front-loaded with main purpose, no redundant information. Efficient and clear.

  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 simple tool (one optional parameter, no output schema, siblings that are consumers), the description fully covers what an agent needs: purpose, filter usage, and integration with other tools.

  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% and the description adds context by linking filter values to model types ('chat' for language models, 'image' for image generation). This adds value beyond the schema description but is minor, so baseline 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 the tool 'List all xAI models available to your account', specifying the verb 'List' and the resource 'xAI models'. It distinguishes from siblings by mentioning they consume the model IDs (ask_grok, generate_image).

  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?

  Explicitly states when to use: 'to discover which models you can pass to ask_grok or generate_image'. Provides filter options but does not explicitly state when not to use, though context is clear.

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

• grok_validateA
  4.2/5.0
  Behavior4/5

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

  No annotations are provided, so the description carries the full burden. It adequately describes outcomes (scored scorecard, identifies weaknesses, returns improved version) and mentions the default model. It could be more explicit about side effects (e.g., does not modify the original artifact), but overall it provides sufficient behavioral disclosure for a non-destructive analysis 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 extremely concise: three sentences covering function, outputs, and usage recommendation. It front-loads the core action and avoids any fluff. Every sentence earns its place.

  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 there is no output schema, the description adequately explains return value (scored scorecard, identified weaknesses, improved version). With 5 parameters (1 required) and no nested objects, the description covers the essential context. It could benefit from mentioning the output format or that the artifact is not mutated, but it is sufficient for an AI agent.

  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 5 parameters thoroughly. The description adds no additional semantic detail beyond the schema (e.g., it doesn't elaborate on valid values for 'rounds' or 'rubric'), making it merely adequate. The default model mention is not a parameter.

  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 runs a validation protocol on any artifact, produces a scorecard, identifies weaknesses, and returns an improved version. The verb 'validate' and resource 'artifact' are specific, and it distinguishes from siblings like 'ask_grok' (Q&A) and 'generate_image' (image generation).

  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 explicitly recommends use as a 'mandatory quality gate before shipping complex work,' providing clear when-to-use guidance. It does not explicitly state when not to use, but the sibling tool list (ask_grok, generate_image, etc.) implies alternatives. The default model mention adds useful context.

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