Compress Query Contexts

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

No annotations are supplied, so the description carries the full behavioral burden. It discloses that the token budget is 'approximate' but says nothing about how selection works, what happens when the budget cannot be met, whether deduplication alters output, or whether inputs are modified.

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?

A single front-loaded sentence with no filler; the core operation and its constraint come first. It is efficient, though the brevity leaves little room for the guidance the tool needs.

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?

Five parameters, zero schema descriptions, and no annotations make this a case where the description must do heavy lifting, and it does not. An output schema exists so return values need no explanation, but selection behavior and the two threshold parameters remain opaque.

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 0% across five parameters. The description only loosely gestures at three of them (query, paragraphs, num_tokens); min_similarity and dedup_threshold — both tunable thresholds with defaults that materially change output — are given no meaning at all.

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 gives a specific verb and resource: 'extract question-relevant sentences,' plus a scoping constraint ('approximate text-token budget'). That is enough to distinguish it from a generic compress_contexts or summarize_text, though it never explicitly contrasts with those siblings.

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?

There is no when-to-use guidance, no exclusions, and no named alternative. An agent must infer that this is for query-focused extraction versus plain compression or summarization purely from the verb 'question-relevant.'

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