local_draft_code

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

With no annotations, the description carries the transparency burden. It does disclose a meaningful behavioral trait: generation happens locally via Ollama and does not consume cloud tokens. However, it does not clarify whether the output is returned as text, written to files, or what happens if Ollama is unavailable, leaving important behavioral details unspecified.

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 concise sentence that front-loads the core action and scope, then adds the key differentiator about local Ollama and cloud-token savings. Every word earns its place and there is no redundant information.

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?

The essential call can be inferred: provide a task description and optionally model, context, or language. The presence of an output schema reduces the need to describe return values. However, optional parameter semantics and tool-selection boundaries are left unclear, so the description is adequate but not fully complete.

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 description coverage is 0%, so the description must compensate by explaining parameter meanings. It does not mention task_description, model, context, or language, and provides no format or usage hints for any of them. The tool-level description allows minimal inference about task_description and language, but not enough to document the parameters properly.

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 uses a specific verb ('Generate') with a clear resource scope: code drafts, boilerplate, unit tests, and scaffolding. It further distinguishes the tool by specifying local Ollama execution and the benefit of not consuming cloud tokens, making it clearly distinct from sibling summarization/extraction tools.

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 phrase 'locally via Ollama without consuming cloud tokens' implies when this tool is appropriate, namely for token-conscious local drafting. However, there is no explicit when-not-to-use guidance or direct comparison to sibling tools, so usage guidance remains mostly implied rather than stated.

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