Dominion Observatory

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

What is this?

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.

Before calling an unknown MCP server → check_trust tells you if it's reliable
After calling any MCP server → report_interaction contributes to the trust network
Every report makes scores better for everyone — this is a collective intelligence system

Tools (8)

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

Quick Start

For agents (MCP)

Connect to: https://dominion-observatory.sgdata.workers.dev/mcp

For developers (REST API)

# Check trust score
curl "https://dominion-observatory.sgdata.workers.dev/api/trust?url=https://example.workers.dev/mcp"

# View leaderboard
curl "https://dominion-observatory.sgdata.workers.dev/api/leaderboard"

# Network stats
curl "https://dominion-observatory.sgdata.workers.dev/api/stats"

How Trust Scores Work

Trust scores range from 0-100 and combine two signals:

Static score (30%): GitHub presence, documentation quality, authentication support
Runtime score (70%): Real success rates, latency, error patterns from agent interactions

Scores above 70 = reliable. Below 30 = risky. The more agents report interactions, the more accurate scores become.

Architecture

Runtime: Cloudflare Workers (330+ global edge locations, <1ms cold start)
Database: Cloudflare D1 (SQLite at the edge)
Protocol: MCP (Model Context Protocol) + REST API
Cost: Runs on free tier

Data Collection

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.

Operator

Built by Dinesh Kumar in Singapore.Part of the Dominion Agent Economy Engine (DAEE).

License

MIT

Dominion Observatory