Retrieve neural model weather forecasts (Google Maps Weather API / WeatherNext 3 / MetNet, ECMWF, GFS, GraphCast) with empirical station bias correction (e.g. KNYC Central Park, KMDW Chicago Midway, KDEN Denver) and strike bracket probability calculations for weather prediction markets.
| Name | Required | Description | Default |
|---|---|---|---|
| target_date | No | ||
| station_or_query | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description implies a read-only operation with 'retrieve' and 'calculation', but it does not explicitly state side effects, rate limits, or data source limitations. No annotations are provided, so the description carries the full burden, but it is not misleading. Some transparency is present but incomplete.
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 a single but dense sentence. It lists many models and capabilities, which adds detail but is somewhat verbose. The structure is clear and not redundant, earning a high score, though it could be trimmed for efficiency.
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?
An output schema exists, so return values need no explanation. However, the description lacks essential context about parameter semantics and when to use the tool relative to siblings. The mention of 'weather prediction markets' gives a hint of use case, but overall completeness is low due to missing parameter guidance.
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 0% and the description does not explain the parameters `station_or_query` or `target_date`. Although station examples (KNYC, KMDW) are given in context of bias correction, they are not linked to the parameters. The description completely fails to clarify parameter meaning, format, or constraints.
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 clearly states the action (retrieve) and the resource (neural model weather forecasts), and lists specific models (MetNet, ECMWF, GFS, GraphCast) to distinguish it from generic forecast tools. It also mentions unique capabilities like bias correction and strike bracket probability, making the purpose highly specific.
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 does not provide any guidance on when to use this tool versus alternatives like foresea_weather_radar or foresea_forecast. It lacks explicit conditions or contrasts that would help an agent select this tool over siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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