generate_file

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

Without annotations, the description must disclose behavior. It reveals it uses a local LLM and offers review vs auto-approve, and token savings. However, it omits details like file overwrite behavior, error handling, or how the options object affects outcomes.

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 a single sentence with three short clauses, making it fairly concise. However, it starts with a redundant title-like phrase 'Local LLM Code Generation' rather than front-loading the core action.

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 nested options and no output schema, the description is insufficient. It fails to explain return values, error conditions, or the behavior of key options like review and backend, leaving the agent underinformed.

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?

The input schema covers all parameters with descriptions (67% coverage, but all key fields described). The description adds context about natural language spec and review/auto-approve, but does not significantly augment parameter understanding beyond the schema.

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 it generates code from natural language using a local LLM, which is a specific verb+resource. However, it does not explicitly distinguish this from sibling tools like modify_file or write_files_atomic, relying on the name to differentiate.

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?

No explicit guidance on when to use this tool vs alternatives. It mentions 'Claude reviews or auto-approves' but does not elaborate on the trade-offs between review modes or when to prefer other file generation tools.

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