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Route Weather Hazard MCP
x402
Route Weather Hazard MCP is a paid API for AI agents from api.m2mcent.com, paid per call via x402, $0.22/call, status unknown (last checked 2026-10-12).
Analyzes weather hazards along a specified route to help agents assess travel risk and plan safe journeys
Price / call$0.22
Success—
StatusUnknown
How this service works
The premier global index of 1,069 monetized MCP nodes across 205 specialized subdomains. Gasless USDC runtime settlements via x402 V2 Spec on Base L2. Save 95% token context.
Output
Returns a structured assessment of weather hazards along the specified route, including hazard types (snow, ice, fog, high winds, flooding, storms), severity levels, affected segments of the route, and timing of hazardous conditions.
Example use cases
Long-haul trucking safety check
“I'm driving a freight truck from Memphis to Dallas tomorrow morning — can you check for any weather hazards along that route, like ice, heavy rain, or high winds?”
Road trip weather risk planning
“We're planning a family road trip from Los Angeles to Las Vegas this Saturday. Can you scan the route for any weather hazards we should be worried about?”
Emergency fleet dispatch routing
“I need to dispatch a repair crew from Seattle to Portland tonight — check the route for weather hazards so I can decide if it's safe to send them out now.”
Technical details
Endpoint
POSThttps://api.m2mcent.com/route-weather-hazard-mcp/api/process?utm_source=zero.xyzWhen to prefer
Choose this endpoint when an agent needs to assess weather-related travel hazards along a specific route or corridor rather than just point-based weather. Prefer this over general weather APIs when the use case involves transportation safety, delivery logistics, or route-based risk assessment where knowing which segment of a journey is dangerous matters.
Side effects
- Invalid or unparseable route data in the 'data' field returns an error
- Extremely long routes or complex waypoint sets may time out
- Route outside supported geographic coverage may return no data
- Vague or ambiguous location descriptions may produce inaccurate results
- Requests for very distant future dates may return low-confidence or empty forecasts
Output
Schema · Response
{
"type": "json",
"example": {
"success": true
}
}Tags
x402
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