ask_local

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

With no annotations, the description bears the full burden. It discloses key traits: one-shot (no conversation state), local execution, no cloud usage, and raw text return. It does not mention error conditions, model availability, or potential latency, but these are secondary for a simple tool. The provided information is accurate and materially helps an agent predict behavior.

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 compact paragraphs. The purpose sentence is front-loaded, followed by usage context and a cost note. Every sentence serves a purpose; no fluff or repetition. Ideal length for a simple tool.

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 description covers purpose, usage, cost, and output format clearly. It omits potential failure modes (e.g., model not installed) and doesn't mention that the model must be pre-pulled, but given the tool's simplicity and the presence of an output schema, these are optional. An agent can safely invoke it with just the required prompt.

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% — all three parameters (prompt, model, system) have descriptions. The tool description adds little beyond the schema: it frames the action as 'one-shot' and mentions 'raw text completion,' which relates to output rather than parameter semantics. This meets the baseline for high schema coverage.

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 opens with a clear verb-resource pair: 'Send a one-shot prompt to a local Ollama model and return its text response.' It distinguishes from siblings by emphasizing 'one-shot' (vs chat_local) and 'raw text completion' (vs specialized extract/summarize tools). The example use cases reinforce its generic scope.

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?

Explicitly states when to use: 'Use for any handoff where the cloud model's full reasoning isn't needed' and lists concrete tasks (drafts, boilerplate, simple extractions, formatting, quick lookups). It also provides a cost rationale ('Runs on the user's own GPU and consumes no cloud-LLM usage'), which guides selection. While it doesn't name sibling alternatives, the categories imply exclusions (e.g., complex reasoning for cloud models, specialized extraction for extract_local).

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