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Snapchat Geo-Intel MCP
x402
Snapchat Geo-Intel MCP is a paid API for AI agents from api.m2mcent.com, paid per call via x402, $0.1/call, status unknown (last checked 2026-10-12).
Processes Snapchat-based geospatial intelligence data to extract location-aware insights from Snapchat content and geographic signals.
Price / call$0.1
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 geospatial intelligence derived from the processed Snapchat data input, including location signals, geographic clusters, or location-tagged content analysis depending on the submitted data.
Example use cases
Geo-trend mapping from Snapchat content
“I have a batch of Snapchat post data — can you extract the geographic intelligence from it and tell me where the highest activity clusters are located?”
Event location detection from social posts
“Here's some Snapchat data from a live event — can you process it to figure out what locations are mentioned or geo-tagged and surface any location intelligence?”
Brand campaign geo-reach analysis
“I want to understand where our Snapchat campaign content is being engaged with geographically — can you process this Snapchat data and give me location-based insights?”
Technical details
Endpoint
POSThttps://api.m2mcent.com/snapchat-geo-intel-mcp/api/process?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to extract or analyze geospatial intelligence specifically from Snapchat-origin data, such as geo-tagged stories, Snap Map signals, or location-tagged social content. Prefer this over generic geospatial APIs when the input is Snapchat-specific and you need social-context-aware location intelligence.
Side effects
- Empty or malformed 'data' string returns a processing error
- Non-Snapchat or unrecognized data format may yield no meaningful geo-intelligence
- Payment failure via x402 protocol returns 402 status before processing begins
- Ambiguous geographic signals in input may result in low-confidence or empty location output
Output
Schema · Response
{
"type": "json",
"example": {
"success": true
}
}Tags
x402
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