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Eco Optimizer MCP
x402
Eco Optimizer MCP is a paid API for AI agents from api.m2mcent.com, paid per call via x402, $0.05/call, status unknown (last checked 2026-10-12).
Analyzes and optimizes data inputs for environmental efficiency or ecological performance metrics
Price / call$0.05
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 an optimized analysis or set of recommendations based on the submitted data string, likely including efficiency scores, identified waste areas, and actionable suggestions for improving ecological or environmental performance.
Example use cases
Energy usage optimization for data center
“I have usage stats from our data center — can you run them through the eco optimizer and tell me where we're wasting energy and how to cut our footprint?”
Supply chain eco performance audit
“Here's our supply chain process data — can you analyze it with the eco optimizer to find the least environmentally efficient steps and suggest improvements?”
Software workload sustainability scoring
“Can you take this workload description and run it through the eco optimizer to get an efficiency score and any recommendations for making it greener?”
Technical details
Endpoint
POSThttps://api.m2mcent.com/eco-optimizer-mcp/api/process?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need automated ecological or environmental optimization analysis on process, operational, or resource data and want actionable efficiency recommendations. Best suited for agents handling sustainability audits, energy management, or green computing workflows where a structured optimization pass is needed over raw data.
Side effects
- Missing or empty 'data' field returns a validation error
- Malformed JSON in request body causes a 400 Bad Request
- Insufficient payment (x402 protocol) results in a 402 Payment Required response
- Overly large data strings may cause timeouts or truncated analysis
- Ambiguous or unstructured data may yield low-confidence or generic recommendations
Output
Schema · Response
{
"type": "json",
"example": {
"success": true
}
}Tags
x402
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