cerebras_instruct

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.

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.

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.

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.

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.

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.