local_ask

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

No annotations exist, so the description must cover behavioral traits. It only states 'no file reading' and hints at local model usage. No mention of safety, side effects, rate limits, or auth. For a mutation-like tool (query), more transparency is needed.

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 sentences, front-loaded with the core purpose, no extraneous words. Efficient and clear about the tool's raison d'être.

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?

With 4 parameters, no output schema, and no annotations, the description is too brief. It lacks details on return format, error handling, token management, and the difference between 'code' and 'light' models.

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%, so baseline is 3. Description adds no parameter-specific semantics beyond the schema—e.g., it doesn't explain the 'model' enum values or 'max_tokens' default implications.

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 poses a free-form question to the local model without file reading, and lists specific use cases (boilerplate, reformulation, translation, commit message, regex). This differentiates it from siblings like local_map, local_digest, which likely involve file processing.

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?

Description provides explicit use cases and notes it's for tasks not worthy of the cloud model. It implies file-reading tasks use siblings, but does not explicitly name alternatives or give when-not guidance.

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