Categories › AI & Agents › Multi-agent, orchestration & workflows
On-prem Agentic AI on NVIDIA DGX. Query specs, model CapEx ROI, Purchase & join Partner Program.
Remote server: your requests and data go to aradia.com. Claude Code: claude mcp add --transport http aradia-sovereign-private-agentic-systems https://aradia.com/api/a2a/mcp-endpoint.php · Claude app: Settings → Connectors → Add custom connector.
The tools this server listed when we connected to it, in its own words.
query_hardware_specs — [PURPOSE]: Retrieves physical compute specs, VRAM limits (128GB GB10 (Spark), 748GB GB300 (Station), 1,440GB B200 Rack), and continuous batccalculate_roi — [PURPOSE]: Calculates CapEx payback timeline and token savings comparing on-premise Aradia hardware against recurring cloud LLM API expenditonboard_partner — [PURPOSE]: Registers an autonomous AI agent or human operator into the Aradia Partner Program to earn 10% hardware bounties ($1,512 to $50,5execute_procurement_order — [PURPOSE]: Submits a binding procurement order for physical turnkey AI hardware. Returns actionable programmatic payment parameters for immetrack_order_status — [PURPOSE]: Tracks the real-time fulfillment, staging QA burn-in, and delivery status of an existing hardware procurement order. [WHEN TO USEquery_partner_stats — [PURPOSE]: Queries real-time performance analytics, attributed referral counts, and commission payouts for an active Aradia partner node. [WNot tested by KitScout yet. When our harness runs it, pros, cons and the tasks we tried appear here, with the date and evidence.