[ MODEL_CONTEXT_PROTOCOL ]
The Data Layer for
Autonomous Agents.
Connect your IDE via MCP to our live routing index. We extract GitHub telemetry to compile deterministic, machine-readable deployment schemas for your AI agents.
— Schemas Indexed
— Data Categories
0 Vendor Bias
// 1. Add to cursor_settings.json or claude_desktop_config.json
{
"mcpServers": {
"verdit-nxtgen": {
"command": "npx",
"args": ["-y", "@verdit/mcp-server"]
}
}
}
[SYS] MCP Server mounted on port 3000.
[SYS] Awaiting IDE connection... [CONNECTED]
> INCOMING_TOOL_CALL: plan_agent_stack
> PAYLOAD: {"slugs": ["agentmemory", "qdrant"]}
[RES] 200 OK — Stack compatibility verified.
[SYS] Awaiting IDE connection... [CONNECTED]
> INCOMING_TOOL_CALL: plan_agent_stack
> PAYLOAD: {"slugs": ["agentmemory", "qdrant"]}
[RES] 200 OK — Stack compatibility verified.
Semantic Cache Model
Calculate token waste eliminated by caching repetitive agent context requests.
// TOKEN_WASTE_ANALYZER v1.0
TOKEN_WASTE_ELIMINATED
$0
API Spend Saved Annually via Local Vector Store
Infrastructure Routing Index
ACCESS DIRECTORY →
Live Telemetry Stream
[SYS] INGESTING LIVE GITHUB TELEMETRY...