Enterprise MCP Gateway
Allows the MCP gateway to trigger and manage n8n automation workflows, including high-risk automations with confirmation, idempotency, and audit protections.
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., "@Enterprise MCP GatewayPull the open support tickets and suggested responses for the top 3 accounts."
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.
Enterprise MCP Gateway
Secure AI-to-business tool infrastructure built on the Model Context Protocol.
A production-oriented MCP gateway that lets an AI agent work with CRM, order, support and automation systems — with authentication, RBAC, a policy engine, prompt-injection defences, audit logging, rate limiting, idempotency, observability, n8n integration and a remote Streamable HTTP transport.
Status: under active construction.
docs/implementation-status.mdrecords exactly which phases are complete and verified. Nothing is claimed here that has not been executed.
What MCP is, in one paragraph
The Model Context Protocol is an open protocol that lets an AI host discover and call capabilities exposed by a server: tools (actions and queries), resources (authoritative read-only context) and prompts (reusable, parameterised instructions). It standardises the wire format so any compliant host can talk to any compliant server. What it deliberately does not do is decide who is allowed to call what — that is the server's job, and that is what this project is about.
Related MCP server: AI-Assisted CRM MCP Server
What this project solves
Connecting an LLM to real business systems is not a tooling problem, it is a trust boundary problem. A naive MCP server hands an agent unmediated access to production data. This gateway inserts the layer a company actually needs:
the caller's identity comes from a verified bearer token, never from a tool argument;
every tool declares its required permission, risk level and audit class in one authoritative registry, and the server enforces it before any business code runs;
high-risk actions (sending customer email, triggering automations) require explicit confirmation and are idempotent;
customer-authored text is treated as untrusted data and is labelled as such when it reaches the model, so "ignore your instructions" in a ticket body is just a string;
every consequential operation lands in an append-only audit trail.
Architecture
AI Host / Agent → MCP Client → Enterprise MCP Gateway → Business systems
observability → audit → authn → authz →
rate limit → policy → idempotency → tool handler
↓
application services
↓
PostgreSQL · Redis · n8n · email adapterFull detail, sequence diagrams and trust boundaries: docs/architecture.md.
Technology
Layer | Choice |
Runtime | Python 3.12+ (developed on 3.14.3) |
MCP | Official Python SDK |
Transports |
|
Data | PostgreSQL via SQLAlchemy 2 + Alembic |
Cache / limits | Redis |
Automation | n8n |
Auth | OIDC/JWT (JWKS), with a separated development mode |
Observability | structlog + OpenTelemetry |
Rationale for each choice, including what was rejected: docs/technology-decisions.md.
Documentation
Document | Contents |
Layering, middleware pipeline, sequences, trust boundaries | |
Stack choices and rejected alternatives | |
Per-phase status, tests run, known limitations |
Security, tool catalogue, deployment, threat model and the demo script are added in their corresponding phases.
Licence
MIT — see LICENSE.
This server cannot be deployed
Maintenance
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