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

No annotations are provided, so the description carries the full burden. It transparently discloses the AI-structured nature of the output, the fields included, the narrative summary, and the explicit exclusions (no article text, no directional trading signal or probability estimate). It does not cover authentication or error behavior, but the schema documents the API key and the operation is a read-style feed.

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 two sentences with no filler. The core purpose is front-loaded, and the second sentence provides important boundary-setting exclusions that help an agent understand the tool's scope. Every clause earns its place.

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 tool with no output schema, the description does a good job of summarizing what the response contains and what it deliberately omits. It also clarifies ticker scoping and the default cross-market behavior. It could add a note on response envelope or pagination, but this is not essential for a correct first 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?

Schema description coverage is 100%, so the baseline is 3; all parameters already have descriptions in the schema. The description adds useful semantics for 'ticker' by explaining optional scoping and the cross-market fallback, but it does not add meaning for 'lang', 'limit', or 'api_key' beyond the schema. This meets the baseline without exceeding it.

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 uses a specific verb and resource: 'Get AI-structured event clusters from recent news.' It specifies the output shape—same-story headline clusters with entity/sentiment/quant-context fields and a narrative summary—and distinguishes the feed from article text or directional signal tools. This makes it clearly distinguishable from siblings like get_sentiment or get_technical_analysis.

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 clear context: optionally scope to one ticker, otherwise get the cross-market feed. It also clarifies what the tool is not for ('not republished article text, and never includes a directional trading signal'), which helps an agent avoid inappropriate use. It does not explicitly name sibling tools or give when-not-to-use alternatives, but the usage context is strong.

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