The full upstream README, mirrored here for reference. Install config, tool schemas, adoption signals, and an original overview live on the Dominion Observatory listing page.
The behavioral trust layer for the AI agent economy.
Check MCP server reliability before you call. Report outcomes to strengthen the trust network.
🌐 Live: https://dominion-observatory.sgdata.workers.dev 📡 MCP Endpoint: https://dominion-observatory.sgdata.workers.dev/mcp
Every AI agent needs to know: "Can I trust this MCP server?" The Dominion Observatory answers that question with real runtime data — not GitHub stars, not static scans, but actual performance metrics from real agent interactions.
check_trust tells you if it's reliablereport_interaction contributes to the trust network| Tool | Description |
|---|---|
check_trust | Get trust score and reliability metrics for any MCP server |
report_interaction | Report success/failure after calling an MCP server |
get_leaderboard | Top-rated MCP servers by category |
get_baselines | Behavioral baselines for a tool category |
check_anomaly | Is this server behavior normal or anomalous? |
register_server | Register a new MCP server (free) |
get_server_history | 30-day trust score trend for a server |
observatory_stats | Overall network statistics |
Connect to: https://dominion-observatory.sgdata.workers.dev/mcp
Trust scores range from 0-100 and combine two signals:
Scores above 70 = reliable. Below 30 = risky. The more agents report interactions, the more accurate scores become.
Started: April 8, 2026
Every interaction reported to the observatory strengthens the trust network for all agents. The behavioral dataset compounds daily — it cannot be replicated by competitors who start later.
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Built by Dinesh Kumar in Singapore. Part of the Dominion Agent Economy Engine (DAEE).
MIT