get_event_catalyst_timeline

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

No annotations provided, so description carries full burden. It effectively discloses response structure (impact ratings, expected direction, historical volatility, trading notes) and time buckets (24h/7d/30d), but omits operational details like data freshness, rate limits, caching behavior, or pagination.

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?

Highly efficient single sentence with zero waste. Front-loaded with the core concept ('Upcoming market-moving events'), followed by concrete examples, then detailed return structure specifications.

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?

Compensates well for missing output schema by detailing return fields (impact ratings, volatility, trading notes) and timeframes. With zero inputs and no annotations, the description adequately covers what the agent needs to know to invoke and handle the response.

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?

Zero parameters present per context signals, which establishes a baseline of 4. No parameter semantics needed in description, and none provided, which is appropriate given the empty input schema.

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?

Excellent clarity: uses specific verb 'Returns' plus concrete resource examples (FOMC, CPI, options expiry, BTC halving) that distinguish this as a scheduled events/calendar tool distinct from general market data or sentiment siblings like get_macro_context or get_sentiment_state.

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?

Lists specific event types (quad witching, political events) that imply appropriate use cases, but lacks explicit guidance on when to prefer this over get_macro_context or get_temporal_context, and includes no 'when-not-to-use' exclusions.

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