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dominion-observatory

vdineshk/dominion-observatory
HTTP
Summary

A trust scoring layer for the MCP ecosystem that lets agents verify server reliability before executing sensitive calls. You get 8 tools: check_trust returns 0-100 scores based on real interaction data, report_interaction feeds the collective intelligence loop, get_leaderboard surfaces top servers by category, and check_anomaly flags behavioral outliers. The gateway proxy mode automatically attaches trust checks and signed attestation receipts to every routed call, which matters for MiCA Article 12 compliance coming July 2026. Runs on Cloudflare Workers with a D1 database tracking 14,820+ servers. Reach for this when you need pre-flight verification before your agent touches unfamiliar infrastructure or when you want compliance attestation for regulated operations.

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On Capafy, your Skill runs online 24/7 as an agent product, and you get paid every time someone uses it.
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Featured
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Monitor with ease. Code with confidence.
Start Free Trial →
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On Capafy, your Skill runs online 24/7 as an agent product, and you get paid every time someone uses it.
Start earning →
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Categories
AI & LLM ToolsMonitoring & ObservabilityData & AnalyticsFinance & Commerce
TransportHTTP
UpdatedApr 8, 2026
View on GitHub

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)

ToolDescription
check_trustGet trust score and reliability metrics for any MCP server
report_interactionReport success/failure after calling an MCP server
get_leaderboardTop-rated MCP servers by category
get_baselinesBehavioral baselines for a tool category
check_anomalyIs this server behavior normal or anomalous?
register_serverRegister a new MCP server (free)
get_server_history30-day trust score trend for a server
observatory_statsOverall 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.

Categories

weather · finance · code · data · search · compliance · transport · productivity · communication

Operator

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

License

MIT

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