vslm_predict

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

With no annotations provided, the description takes on the full burden and delivers rich behavioral detail: it preserves original ordering, provides counts, exposes whether the topic came from the server or the caller, and identifies the model head via model_id. The '~2000 per call is comfortable' note adds practical capacity guidance. Nothing contradicts external signals.

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 organized into Purpose, Usage, Args, and Returns sections, making it scannable. Every sentence adds useful information, from the token-spend motivation to the exact return fields. It is detailed but not verbose, and the most important usage guidance appears immediately after the one-line purpose.

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 annotations and no schema descriptions, the description covers everything an agent needs: what the tool does, when to use it, how to format each parameter, scale expectations, and the complete return structure. The only minor omissions are error behavior and rate limits, but those are not essential to correct invocation.

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?

The input schema has 0% description coverage, yet the description fully compensates. It explains that sentences are raw inputs, suggests a comfortable batch size, and clarifies query as a natural-language topic with concrete examples ('AI capex plans', 'rate cuts'). This is materially more than the bare 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 opens with a specific, action-oriented statement: 'Filter a list of sentences to only those relevant to `query`.' This clearly identifies a verb, target resource, and selection criterion. Even without siblings, the scope is unambiguous and distinct from generic 'predict' or 'classify' tool interpretations.

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?

The description explicitly says when to use the tool: 'Use this BEFORE feeding noisy context ... to an LLM' and explains the benefit of cutting token spend. It also advises chaining it with a language model. However, it does not state when not to use it or mention any alternatives, so it falls slightly short of full 5-level guidance.

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