codebrain_generate

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

With no annotations provided, the description carries full burden. It discloses that the response is raw text and advises reviewing before applying. It also explains the optional system message and use_brain flag, offering good insight into tool behavior without omissions.

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?

The description is concise (approx. 100 words), front-loaded with purpose, and structured as a brief intro followed by parameter explanations. Every sentence adds value without redundancy, making it easy for an agent to parse quickly.

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 tool's complexity and the presence of an output schema (though not shown), the description covers the core aspects: what it does, when to use it, parameters, and output nature. It omits potential limitations (e.g., model capabilities) but is generally sufficient for selection and invocation.

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?

Input schema has 0% description coverage, so the description must compensate. It explains the 'prompt' as task description, 'system' as steering message, and 'use_brain' as prepending context. This adds essential meaning beyond the schema's bare titles, though it could include format hints.

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 that the tool delegates a generation task to a local Qwen-Coder model via Ollama, providing specific use cases. It distinguishes the tool's role for bulk or routine work, but does not explicitly differentiate from siblings like codebrain_batch_generate or codebrain_generate_verified, which limits clarity of its niche.

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 gives context for when to use the tool ('bulk or routine work where a 14B local model is good enough') and lists example tasks. However, it does not specify when not to use it or mention alternative sibling tools, leaving the agent without explicit decision boundaries.

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