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

Annotations already declare readOnly, idempotent, openWorld, non-destructive, so the safety profile is covered. The description adds real behavioral context beyond that: it is structured fact extraction rather than republished article text, and it never emits a directional trading signal or probability estimate — a meaningful expectation boundary for an agent.

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?

Three tight sentences, front-loaded with what is returned, then scope behavior, then content boundaries. No filler or repetition of schema defaults, and each sentence carries distinct information.

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?

With no output schema, the description compensates by listing the returned fields (entity/sentiment/quant-context, narrative summary) and the cluster structure, and it defines scope behavior and content limits. Combined with a fully documented schema, an agent has everything needed to call it 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?

Schema coverage is 100%, so the baseline is 3. The description adds semantic meaning by explaining the omissible ticker's runtime consequence ('otherwise returns the latest cross-market feed'), clarifying the default output scope rather than merely restating the parameter.

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?

States a specific verb and resource ('Get AI-structured event clusters from recent news') and enumerates the payload (headline clusters, entity/sentiment/quant-context fields, AI-written narrative summary). It is clearly distinguishable in substance from market-map or fundamentals siblings, but it never names or contrasts a sibling tool explicitly, so it stops short of a 5.

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?

Gives one conditional usage rule — pass a ticker to scope, omit it for the latest cross-market feed — which is genuinely useful. However, there is no guidance on when to prefer this over get_market_events or get_sentiment, and no exclusions or prerequisites beyond the optional scope.

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