summarize_local

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

No annotations are provided, so the description carries full burden. It mentions handling long text and returning a structured summary, but does not disclose whether the tool has side effects, requires permissions, or other behavioral traits critical for safe invocation.

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?

Concise two-paragraph structure with clear front-loaded purpose and bulleted parameters. No extraneous text, but the missing model parameter could be noted.

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 an output schema exists and no annotations, the description covers use cases and two of three parameters, but missing model documentation and behavioral disclosure leaves it incomplete overall.

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 0% yet the description adds meaning for 'text' and 'focus' via the Args section. However, the 'model' parameter is undocumented, leaving a gap.

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?

Description clearly states it summarizes text using a local model, positioning it as a cheap offload for long files. Use cases are listed, but it does not explicitly differentiate from sibling tools like ask_local or chat_local, which may also handle text.

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?

Describes when to use (long files, logs, etc.) and implies cost saving over cloud model, but does not provide explicit when-not or alternative recommendations among sibling tools.

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