Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@MCP Engineering Agent Show the audit log for today's authorization decisions."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
A security-first engineering agent demonstrating an MCP-style JSON-RPC tool boundary. It separates model orchestration from typed tools, authorization, and audit events.
User -> Agent -> JSON-RPC/MCP Boundary -> Tool Registry -> Authorization -> Tool
`-> Audit EventRelated MCP server: Enterprise MCP Gateway and Tool Registry
tools/list and tools/call JSON-RPC methods
Typed tool registration and discovery
Role-based authorization before tool execution
Read-only vs write-capable tool separation
UTC audit events for every authorization decision
Framework-light design that is easy to extend to a full MCP server
Agentic systems become risky when an LLM can directly execute arbitrary actions. This project treats tools as a privileged interface with explicit schemas and authorization boundaries.
pip install -e '.[dev]'
python -m app.server
pytest -qNo real credentials or external systems are required for the demo.
This server cannot be deployed
Zero-secret MCP gateway for AI agents: risk-scored, audited calls with human-in-the-loop approval.
Control plane for autonomous software labor. Agents claim objectives over MCP with audit trail.
Runtime permission, approval, and audit layer for AI agent tool execution.
Identity, authorization, audit trails, and revocable permissions for AI agents accessing MCP tools.
