io.github.vdineshkPublished by GitHub account vdineshkv1.0.0Updated Oct 24, 2026
Regulatory intelligence for AI agents across jurisdictions
The publisher's own server: Observatory
Regulatory intelligence for AI agents across jurisdictions, letting an assistant look up compliance obligations, articles, deadlines and cross-border comparison
Web executable via Streamable HTTP. Remote servers run from the web runtime once configured in a workspace.
Paste this prompt into Claude Code, Codex, Cursor or another agent — or into SourceWeft chat. The agent reads this server's install guide, shows you its publisher, endpoint and sign-in, and adds it to your client once you agree. Keys never go into a command.
Read https://sourceweft.com/mcp/io.github.vdineshk%2Fai-compliance-monitor/install.md and connect the MCP server it describes to my client. Before changing any config, show me its publisher, endpoint and sign-in method and wait for my OK. Never put an API key in a command or in a file I would commit; ask me where it should come from.Add this to your client's mcpServers config.
{
"mcpServers": {
"ai-compliance-monitor": {
"type": "http",
"url": "https://ai-compliance-monitor.sgdata.workers.dev/mcp"
}
}
}Structured regulatory intelligence for AI agents operating across jurisdictions.
The first MCP server purpose-built for AI agent compliance. Check obligations, deadlines, and cross-jurisdiction requirements for the EU AI Act, Singapore IMDA Agentic AI Framework, and Colorado AI Act.
AI agents operating across borders face a fragmented regulatory landscape. The EU AI Act requires automatic logging (Article 12). Singapore's IMDA framework mandates escalation protocols. Colorado demands impact assessments. No existing tool maps agent behavior to specific regulatory requirements across jurisdictions.
This server provides machine-readable, structured regulatory intelligence that AI agents can query at runtime to understand their compliance obligations.
npm install -g wrangler)CLOUDFLARE_API_TOKEN and CLOUDFLARE_ACCOUNT_ID environment variablesThis server feeds behavioral interaction data to the Dominion Observatory, the trust scoring layer for the AI agent economy.
MIT
Built by @vdineshk | Singapore
Source: README.md at commit b1352c8
Given an AI agent use case and optional jurisdiction, returns all applicable regulatory obligations with evidence requirements, enforcement dates, and penalty information. Use this to understand what compliance requirements apply to a specific type of AI agent.
use_caserequiredjurisdiction_codeReturns structured, machine-readable regulation details including specific articles, obligation classifications, risk levels, and evidence requirements. Use this to understand the detailed requirements of a specific regulation.
categoryregulation_idrequiredReturns upcoming and recent compliance deadlines with enforcement dates, penalty information, and related obligations. Use this to understand time-sensitive compliance requirements.
months_aheadinclude_passedjurisdiction_codeSide-by-side comparison of regulatory obligations across jurisdictions for a given compliance category. Shows equivalent, overlapping, stricter, and weaker mappings between regulations. Use this to understand how requirements differ across borders.
categoryrequiredjurisdictions90% of the applicable checks met
How complete this listing is, computed from the registry entry, the endpoint and public sources. Not a rating of the server and not a statement that it is safe or working today: reachability is a dated check, and no installs or reviews go into the score.
Declare a license in the repository or the registry entry.
com.dominionobservatoryOfficial publisher
Remote MCP service that scores the behavioral trust of other MCP servers and skills, so an assistant can check reliability before calling unknown tools.
Published by GitHub account vdineshk
A remote trust-scoring service that lets an assistant check an MCP server's reliability, verify signed attestations, and report call outcomes.
Server URL: https://ai-compliance-monitor.sgdata.workers.dev/mcp# Check obligations for a hiring agentcurl "https://ai-compliance-monitor.sgdata.workers.dev/api/obligations?use_case=hiring_screening"
# Get EU AI Act requirementscurl "https://ai-compliance-monitor.sgdata.workers.dev/api/regulations?regulation_id=eu-ai-act"
# Check upcoming deadlinescurl "https://ai-compliance-monitor.sgdata.workers.dev/api/deadlines"
# Compare transparency requirements across jurisdictionscurl "https://ai-compliance-monitor.sgdata.workers.dev/api/compare?category=transparency"
# Service statscurl "https://ai-compliance-monitor.sgdata.workers.dev/api/stats"# Clone the repogit clone https://github.com/vdineshk/ai-compliance-monitor.gitcd ai-compliance-monitor
# Install dependenciesnpm install
# Create D1 databasenpx wrangler d1 create ai-compliance-monitor-db# Copy the database_id from output into wrangler.toml
# Run migrationsnpx wrangler d1 migrations apply ai-compliance-monitor-db --remote
# Seed regulatory datanpx wrangler d1 execute ai-compliance-monitor-db --remote --file=./migrations/0002_seed_data.sql
# Deploynpx wrangler deploynpx wrangler deploy --dry-run