Ask the local model

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

With no annotations provided, the description carries the full burden and does an excellent job: it discloses the local/offline nature (nothing leaves the machine), no API cost, no file access, cannot run anything, roughly 16 tok/s for a 7B model, and that no conversation context is carried over. This is rich behavioral context that goes well beyond any schema.

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 dense but efficiently structured: it opens with the core action, then capability boundaries, then usage examples, then constraints, then alternatives. Every sentence earns its place and there is zero filler or repetition.

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 there is no output schema and no annotations, this description is remarkably complete: it covers purpose, constraints, performance characteristics, safety boundaries, and alternatives. For a parameter-rich tool (5 params) it fully compensates for the missing structured metadata.

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 100%, so the baseline is 3. The description adds value by naming maxTokens as the main latency lever and relating it to the tok/s speed, which connects the parameter to a concrete performance behavior beyond mere syntax. It doesn't cover every parameter explicitly but the schema already handles most of that burden.

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 states exactly what the tool does: sends a prompt to a local model via Ollama and returns the answer immediately. It clearly identifies the resource (local model) and verb (send prompt / get answer), and differentiates it from siblings by noting it is the local-model option distinct from local_classify and local_models.

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?

Provides explicit when-to-use guidance ('summarising long output, drafting boilerplate or commit messages, extracting fields from text') and explicit when-not-to-use with named alternatives ('For work needing judgement, repo context, or edits on disk, do it yourself or use codex_start'). Also gives practical guidance on keeping outputs short and mentions the CPU speed constraint.

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