Dominion Observatory

by vdineshk

1 stars
363 downloads
Not rated
GitHub

Description

# 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:**…

About

# 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…

Details

Author
vdineshk
GitHub stars
1
Downloads
363
Categories
Other, AI, Security, Infrastructure

- check_trust retrieves trust scores and reliability metrics for any MCP server.
- report_interaction reports success or failure after calling an MCP server.
- get_leaderboard displays top-rated MCP servers by category.
- check_anomaly determines if server behavior is normal or anomalous.
- register_server allows free registration of new MCP servers.
- get_server_history shows a 30-day trust score trend for a server.

Setting up with Highlight

This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:

  1. Download and install Highlight from highlightai.com/download
  2. Navigate to the plugins tab and select "Add Custom Plugin"
  3. Configure the plugin with the settings below
    Plugin Name Dominion Observatory
    Command (node, npx, python, etc.)

    Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.

  4. Enable "Start Automatically" if you want the plugin to start when Highlight launches

From the repository

Agents connect via the MCP endpoint https://dominion-observatory.sgdata.workers.dev/mcp. Developers can also use the REST API endpoints for trust checks, leaderboard, and network stats.

Claude Desktop / Cursor

Paste into your MCP client config file to install this server.

{
    "mcpServers": {
        "dominion observatory": {
            "dominion-observatory": {
                "url": "https://dominionobservatory.com/mcp"
            }
        }
    }
}

McpServers

{
    "dominion-observatory": {
        "url": "https://dominionobservatory.com/mcp"
    }
}

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.

- Beforecalling an unknown MCP server →check_trusttells you if it's reliable
- Aftercalling any MCP server →report_interactioncontributes to the trust network
- Every report makes scores better for everyone— this is a collective intelligence system

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

# 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"

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.

- 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

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.

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

Built byDinesh Kumarin Singapore. Part of the Dominion Agent Economy Engine (DAEE).

Behavioral trust scoring for MCP servers and AI agents. Live registry tracking 4,500+ servers with trust scores based on interaction history, success rates, and latency.

Prompt analytics MCP server: score prompts, search history, detect leaked credentials, and scan AI coding sessions.

AI health, token usage, LLM cost optimization, BYOK vault, and cleanup audits for MCP agents.

Enforces organisational AI usage policies at the agent layer — blocks prohibited model calls, enforces data residency rules, logs policy violations, and ensures AI governance policies are machine-executable.

Behavioral trust scoring for 14,820+ MCP servers. Check reliability, latency, and success rates before tool calls.

Guardrails service for AI agents. Default-deny tool call evaluation with LLM safety analysis, priority-ordered decision matrix, and human-in-the-loop escalations. Session recording, behavioral analysis, MCP proxy, secret redaction, and real-time audit.

MCP server for Langfuse — query traces, debug errors, analyze sessions and prompts from any AI agent

Structural observability for AI conversations. Detects loops, stuck states, and convergence patterns across 17 channels without analyzing content.

AI-powered security operations with Wazuh SIEM + Claude Desktop. Natural language threat detection, automated incident response & compliance.

Interact with the RAD Security platform which provides AI-powered security insights for Kubernetes and cloud environments.

No reviews yet — be the first

Sign in to leave a review

Use Google, GitHub, or an email account so ratings stay tied to real people.

Email sign in

No reviews posted yet.