Ai Self Audit MCP Server by MEOK AI Labs
Ai Self Audit MCP Server by MEOK AI Labs. Exposed over MCP by the ai mcp server, that capability becomes something an assistant can invoke while it works, not something you go and do afterwards.
AI self-audit MCP — AI agents audit their own EU AI Act compliance in real time with signed certificates.
Everything the assistant can do here goes through one of these:
HMAC — signed audit logsHash — chained integrityTamper — proof recordsPro — ** $99/mo — Full MCP suite + EU AI Act trackingEnterprise — ** $499/mo — Custom dev + SLA + Dedicated supportDomain — PurposeTier — PriceThe server ships on npm as @smithery/cli, so your MCP client can launch it on demand — there is no separate build step. Add the server block to your client's configuration, restart it, and the tools register themselves.
This sits in the monitoring and observability group, where several servers overlap in what they claim to do but differ sharply once you actually set them up. Ai's toolset — HMAC, Hash, Tamper and 4 more — is a fair guide to whether it matches your workflow. It is maintained by CSOAI-ORG; worth a glance at recent repository activity before you build anything load-bearing on it.
We check each listing at SyncDev against the project's documentation before it goes live — if something here drifts out of date, it is a bug worth reporting.
| Tool | What it does |
|---|---|
| HMAC | signed audit logs |
| Hash | chained integrity |
| Tamper | proof records |
| Pro | ** $99/mo — Full MCP suite + EU AI Act tracking |
| Enterprise | ** $499/mo — Custom dev + SLA + Dedicated support |
| Domain | Purpose |
| Tier | Price |
{
"mcpServers": {
"ai-self-audit-mcp": {
"command": "uvx",
"args": ["ai-self-audit-mcp"]
}
}
}Configuration as documented by the project. Restart the client after saving.
Give your coding agent the full DevTools toolbox: traces, network, console, heap snapshots and Lighthouse.
Dashboards, Prometheus and Loki queries, incidents and alerts — observability by conversation.
Errors with full context — stack traces, issue triage and AI-powered root-cause analysis from Sentry's server.
Enables enhanced web research capabilities for large language models through intelligent search queuing and advanced content extraction.
Automates browser interactions and enables Large Language Models (LLMs) to interact with web pages through Playwright and Chrome DevTools Protocol
Guides tool usage by providing recommendations for MCP tools at each problem-solving stage.