omni-ai-routing-engine
Enables L402-compliant Lightning Network payments through Alby for micro-monetization of MCP calls.
Provides endpoint telemetry and health checks for Cloudflare edge nodes, using Cloudflare internal routing for low-latency latency information.
Monitors and evaluates health and routing for OpenAI models (e.g., gpt-4o) by testing latency and failure risk.
This server does not currently have a deployable release on Glama. Follow the repository's setup instructions to install and configure it for your MCP client, including any required credentials and local dependencies.
Omni-Perspective AI Health & Routing Engine (MCP Server)
A high-performance, edge-deployed Model Context Protocol (MCP) server running on Cloudflare Workers. It acts as a predictive decision engine and circuit-breaker orchestrator for AI agents, multi-model consensus systems, and autonomous workflows (Council System v1).
π©οΈ Architecture & Capabilities
Low-Latency Telemetry: Instant edge latency checks via Cloudflare internal routing.
Failover & Circuit Breaker: Dynamically evaluates network degradation to issue rerouting directives (
PROCEEDvsFAILOVER_TO_SECONDARY).M2M Lightning Paywall (L402 / x402 Ready): Integrated with Lightning Network addresses (
admiredhut112@walletofsatoshi.com) for micro-monetization (1 to 5 SATS per call).llms.txtNative: Exposes machine-readable documentation at/llms.txtfor autonomous agent discovery.
Related MCP server: genpark-edge-geo-distributed-request-routing-governor-skill
π οΈ Tools Provided
get_endpoint_telemetry (Cost: 1 SAT)
Atomic telemetry check returning live latency and health metrics directly from the edge node.
Input Parameters:
target_provider(string, optional): Network provider or node identifier to evaluate (e.g.,cloudflare-edge).
get_health_and_routing (Cost: 5 SATS)
Predictive decision engine returning failure risk, network degradation, circuit-breaker directives, and M2M failover routing consensus.
Input Parameters:
target_provider(string, optional): Target model ID to test latency against (e.g.,openai/gpt-4o,anthropic/claude-3-5-sonnet).risk_threshold(string, optional): Sensitivity threshold (STRICT,BALANCED,PERMISSIVE).
π Quick Setup & Integration
1. Claude Desktop Integration
Add the following snippet to your claude_desktop_config.json:
{
"mcpServers": {
"omni-perspective-engine": {
"command": "npx",
"args": [
"-y",
"@smithery/cli@latest",
"run",
"shrill-wind-8acb.hivanapps.workers.dev",
"--config",
"{}"
]
}
}
}2. Cursor / Windsurf Integration
Go to Settings -> MCP Servers -> Add New MCP Server:
Type:
SSEorHTTPURL:
https://shrill-wind-8acb.hivanapps.workers.dev/mcp
π Standard Endpoint Discovery
MCP Endpoint:
https://shrill-wind-8acb.hivanapps.workers.dev/mcpLLM Context:
https://shrill-wind-8acb.hivanapps.workers.dev/llms.txt
π License
MIT License
Tool Schema Changelog
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Maintenance
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Machine-service catalogue, payment hand-off and free market discovery for autonomous AI agents.
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