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

With readOnlyHint=true already covering the safety profile, the description adds real behavioral context beyond annotations: per-call cost ($0.005/call), a 100 free calls/day quota, and the shape of what is returned (direction, confidence, horizon, tradable_now/next_open). It does not cover pagination or latency, so it falls short of a full behavioral picture.

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?

It is front-loaded with what the tool returns, then usage, then filtering, then cost — a logical order with no filler sentences. It is dense and runs long for a single paragraph, but each clause (event kinds, impact fields, pricing) 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?

With no output schema, the description carries the return-value burden and does so by describing the per-asset impact fields and tradable_now/next_open flags. Combined with 100% schema coverage on all 8 parameters and stated cost/quota, 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 description coverage is 100%, so the baseline is 3, but the description adds practical, non-schema meaning: that past `since` values backtest, and that min_confidence>=0.4 is a sensible acting threshold while universe=["NVDA","BTC"] narrows impacts. That is useful guidance over and above the field docs.

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 ('List') and resource ('market-moving events since a point in time') and enumerates the event kinds covered, so the scope is unambiguous. It stops short of naming or contrasting the relevant siblings (impact_for, exposure_graph), so an agent cannot fully rule out overlap from the text alone.

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 supplies concrete use cases ('what happened in the last N hours that affects my book', and backtesting with a past `since`) plus operational filtering advice (universe=["NVDA","BTC"], min_confidence>=0.4). It does not state when NOT to use this tool or name the alternative to reach for instead, so it is clear context without exclusions.

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