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

With no annotations, the description carries the full load and does so well: it discloses the 15-minute staleness, the free/x402 pricing, the calibration gate that suppresses signals for uncalibrated classes, and the ranking/return fields (divergence, direction, model confidence from ensemble spread, liquidity, parse_coverage). It leaves unaddressed error behavior and result cutoff semantics, but the operational profile is unusually well documented.

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?

Front-loads the core purpose, then layers staleness, pricing, and gating in tight sentences; the trailing HTTP/pricing/docs block is compact and useful. The only blemish is the truncated leading fragment ('...weather p…'), likely an artifact, which slightly muddies the opening.

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 no-annotation, no-output-schema tool it covers purpose, routing, staleness, pricing, gating, and the main response fields, so an agent has enough to call it correctly. Pagination behavior and empty-result handling are the notable omissions, which keep it short of a 5.

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 50% — hazard and region are documented in the schema, while limit and min_divergence rely on their names and numeric bounds. The description adds no parameter-level meaning (e.g., how limit interacts with ranking, or what min_divergence filters on), so it neither compensates for the gap nor detracts.

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+resource (compares weather-model event probability vs market-implied probability across weather-linked prediction markets) and explicitly distinguishes itself from its closest sibling by naming the /v1/edge/weather-markets endpoint as the realtime counterpart. An agent can route between the two without opening either schema.

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?

Explicitly frames the selection condition: this is the free, 15-minute-stale variant, and 'Use the paid endpoint for realtime data.' It also notes the emission gate (only calibrated market classes). It stops short of stating when not to use it beyond the free/realtime tradeoff, but the routing guidance is clear and actionable.

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