Server Quality Checklist

Tool Scores

• cerebras_complexB
  3.2/5.0
  Behavior3/5

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

  No annotations provided, so the description must convey behavioral traits. It mentions the model size and intended use but does not disclose potential trade-offs like speed, cost, or limitations. Lacks detail on side effects or return behavior.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness4/5

  Is the description appropriately sized, front-loaded, and free of redundancy?

  Two concise sentences that communicate the essential purpose and scope. Could be slightly more structured but appropriate for the length.

  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?

  No output schema, so description should clarify return values or side effects. It implies output is written to file_path but doesn't explicitly state. Lacks completeness for a tool with 5 parameters and no annotations.

  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 parameters are documented. Description adds guidance for the prompt parameter but does not enhance understanding of other parameters beyond what the schema provides.

  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?

  Description clearly states it is for heavy-duty code generation using Cerebras gpt-oss-120b, with specific use cases like multi-file features, CRUD APIs, and complex components. However, it does not explicitly differentiate from siblings beyond implying heavier tasks.

  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?

  No explicit guidance on when to use this tool versus alternatives like cerebras_quick or cerebras_auto. The description implies it's for complex tasks but does not state when not to use it or provide any exclusion criteria.

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

• cerebras_instructB
  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 must disclose behavioral traits. It mentions model size and hidden reasoning format but fails to cover key behaviors like rate limits, error handling, file creation/modification behavior, or response format. This is minimal disclosure for a code generation tool.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness4/5

  Is the description appropriately sized, front-loaded, and free of redundancy?

  Two sentences are concise and front-loaded with the core purpose. No unnecessary words. However, it could be slightly more structured by separating model info from usage guidance.

  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 a code generation tool with 5 parameters and no output schema or annotations, the description is incomplete. It lacks details about output format, error states, file write behavior, and how context_files are used. This leaves significant gaps for the 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 coverage is 100%, so the baseline is 3. The description adds no extra meaning to parameters beyond what the schema already provides. It focuses on the model's purpose rather than parameter details, so it does not improve or worsen parameter understanding.

  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 'Instruction-following code generation' with a specific verb (generate code) and resource (using Cerebras model). It distinguishes from sibling tools by emphasizing 'precise instruction following, documentation-heavy code, typed interfaces, and detailed specs', setting it apart from cerebras_auto, cerebras_complex, etc.

  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 instruction-following and documentation-heavy code, but lacks explicit guidance on when to use this versus siblings. It does not specify when not to use it or provide clear alternatives, leaving the agent to infer from context.

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

• cerebras_quickA
  3.5/5.0
  Behavior2/5

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

  No annotations are provided, so the description must disclose behavioral traits. It mentions speed and model size but does not reveal that the tool likely writes to files (based on file_path parameter) or any side effects, permissions, or rate limits. Key behavioral information is missing.

  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 with three short sentences, front-loading the purpose and usage. No unnecessary words or repetition.

  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 lack of output schema and 5 parameters, the description is brief. It does not explain the tool's action (e.g., whether it writes files directly or returns code) or what the output looks like. Basic contextual details are missing.

  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 no significant meaning beyond the schema; it merely restates the tool's speed. The schema already adequately describes the parameters.

  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 is for fast code generation using a specific model, targeting simple edits, boilerplate, and single-function generation. It distinguishes from siblings by emphasizing trivial tasks where speed matters, though it does not explicitly contrast with other cerebras tools.

  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 to use this tool for trivial tasks where speed is most important. It implies alternatives exist for more complex tasks but does not name them or specify 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.

• cerebras_reasoningA
  4/5.0
  Behavior3/5

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

  Discloses model name, parameters, reasoning format hidden. However, no annotations exist, and description fails to mention whether tool modifies state (file_path suggests write), potential costs, or rate limits. Moderate 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?

  Single sentence with two clauses, zero waste, front-loaded with core purpose. Highly concise.

  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?

  No output schema, but schema covers parameters well. Description focuses on model capabilities; missing output format details, but overall sufficient for usage given sibling comparison and parameter schema.

  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 detailed descriptions. Description adds no extra information about parameters, 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?

  Description explicitly states verb (generate code), resource (Cerebras zai-glm-4.7), and distinguishes from siblings by emphasizing 'most powerful for deep reasoning' vs other cerebras tools.

  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?

  Clearly states when to use: for algorithms, architecture decisions, advanced logic, deep reasoning. Implicitly contrasts with siblings, but no explicit when-not-to-use or alternative naming.

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

• cerebras_autoA
  4.1/5.0
  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 the model selection logic (8B, 120B, 357B, 235B based on task type), which adds transparency. However, it does not mention potential side effects or output characteristics.

  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, front-loaded with the core auto-selection feature, and contains no redundant 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?

  The description explains the purpose and selection logic well, but lacks information about return values or error handling. Given the moderate complexity (5 params, no output schema), it is adequate but not 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%, so the description adds minimal value beyond the schema. The description mentions 'code generation instructions' but does not provide additional meaning beyond the parameter 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 auto-selects the best Cerebras model based on prompt complexity, and it distinguishes from sibling tools by explaining the selection strategy (simple->8B, complex->120B, etc.).

  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 advises to use this tool when unsure which model fits, providing clear context. It does not explicitly list when not to use it, but the sibling tool names imply alternatives for specific scenarios.

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