summarize_text

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

With no annotations provided, the description carries the full burden for disclosing behavior. It mentions the AI model, implying an external API call with potential latency/cost, but it does not disclose limitations, output format flexibility, or side effects. Notably, it claims 'structured bullet points' while the schema's 'format' parameter allows 'paragraph', creating an inconsistency in behavioral expectations.

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 a single, front-loaded sentence with no filler words. It efficiently conveys the core purpose and a distinctive aspect (AI model). This is an optimal length for a tool description.

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?

The one-sentence description is insufficient for a tool with no annotations and no output schema. It lacks usage context, behavioral caveats, and details about output variations (e.g., paragraph vs. bullets). Given the tool's moderate complexity (3 parameters), more context is needed to safely invoke 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?

The schema description coverage is 100%, so the parameters 'text', 'format', and 'maxLength' are already well-described. The description adds no extra semantic meaning beyond 'structured bullet points', which does not clarify how 'format' or 'maxLength' affect output. Baseline 3 applies because the schema does the heavy lifting.

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 summarizes text and specifies the output style ('Executive TL;DR', 'structured bullet points') and the underlying model ('Workers AI Llama 3.1'). This distinguishes it from sibling tools like x_sentiment or dex_price_summary, which are also potentially summarizers but with different purposes.

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 provides no guidance on when to use this tool versus alternatives, nor does it mention any exclusions or prerequisites. Sibling tool names are available, but the description does not reference them or set usage boundaries. It only states what it does, leaving the agent to infer when to use it.

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