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

Annotations already mark this read-only and idempotent, and the description goes well beyond them by disclosing the 16K character ceiling, the 3-5 bullet output shape, JSON return format, per-call pricing, and the x-credit-token/auth model. This adds genuinely useful behavioral context and does not contradict the annotations.

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 core transformation is front-loaded and the pricing/auth details are relevant to correct invocation, so they earn their place. The prose is dense and promotional, which slightly reduces scannability, but no sentence is wasted.

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 simple one-input summarization tool with an output schema and rich annotations, the description covers input, output shape, size limits, cost, authentication, and usage context. Nothing critical is missing for an agent to select and invoke the tool correctly.

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 already provides 100% coverage for the single text parameter, so the baseline is 3. The description adds meaningful constraint information beyond the schema, namely the 16K character limit and the expected JSON bullet output. For a one-string-parameter tool, this is sufficient and helpful.

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 a specific job: compress arbitrary text into 3-5 bullet points, with an explicit 16K character limit and clean JSON output. It also distinguishes itself as the 'digest step' for agent pipelines, which sets it apart from siblings like ai_extract or ai_translate. The purpose is unambiguous and actionable.

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 explicitly frames when to use the tool: as a digest step for agents that need to compress content they can't carry in context. It also clarifies entry requirements ('no API key, no prompt engineering'), which is useful. However, it does not name alternatives or state when not to use it, leaving some inference to the agent.

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