ask_local

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

No annotations are provided, so the description carries the full burden. It discloses that it's a one-shot, runs locally on the user's GPU, and consumes no cloud usage. It doesn't mention potential errors or prerequisites like needing a running local Ollama instance, but it's adequately transparent for typical use.

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 (two short paragraphs and a bullet list), front-loaded with the purpose, and each sentence earns its place. No redundancy or fluff.

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 presence of an output schema (not shown but indicated), the description does not need to detail return values. It covers use cases, parameters, and local execution. However, it omits prerequisites like having Ollama installed or the model available, which is a minor gap.

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%, but the description includes an Args section that adds meaning: 'prompt: The task / question', 'model: Override the default model', 'system: Optional system prompt to shape behavior.' This significantly aids understanding beyond the schema titles.

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 the tool sends a one-shot prompt to a local Ollama model and returns the response. The verb and resource are specific, and it distinguishes from sibling tools like chat_local (multi-turn) and extract_local (specialized).

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 explicitly lists use cases (drafts, boilerplate, simple extractions, formatting, quick lookups) and explains when to use it instead of cloud models: when full reasoning isn't needed, and it consumes no cloud-LLM usage. This provides clear guidance.

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