distill_context

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

With no annotations, the description carries the burden and is fairly transparent: it states zero cloud LLM token cost, an output size, and a compression ratio. It does not disclose edge-case behavior or detailed return structure, but the core behavior is clearly conveyed.

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 concise sentences, front-loaded with the core purpose and followed by concrete performance traits. No filler.

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?

For a two-parameter tool with no output schema, the description tells the agent what it produces, roughly how much output to expect, and why it is cheap. It could specify the output shape more precisely, but nothing critical is missing for selecting and invoking it.

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 the schema already documents raw_text and max_keywords. The description adds general context about entity/metric/keyword extraction but adds little parameter-level detail beyond what the schema provides.

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 names a specific verb ('distills'), a clear resource ('raw text'), and the outputs (entities, numerical metrics, salient keywords). It also quantifies compression, making it easy to distinguish from sibling text utilities such as extract_markdown or sanitize_pii.

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?

It doesn't name alternatives, but it communicates a clear use context: low-cost/zero LLM-token local distillation of long text. This is enough for an agent to select it over lookup/verify/sanitize tools, though explicit when-not guidance is absent.

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