Managed Model Context Protocol as a Service

Bring Your Own AI.

Connect any AI to any system through secure, managed MCP infrastructure. Keep the AI tools your team already uses. Keep your existing systems. Let us run the protocol layer in between.

14-day trial · No credit card · Vendor neutral

# Add your hosted MCP server to any AI client { "mcpServers": { "acme-support": { "url": "https://mymcpaas.com/mcp/srv_8fk2n", "auth": "oauth" } } } # Your AI can now use the tools you approved, # against the systems you connected — and every # call is logged, scoped, and revocable.

We are not another AI model.

We are the hosted layer that lets the AI you already chose work safely with the systems you already run. No lock-in, no rebuild, no protocol plumbing to maintain.

Vendor neutral by design

ChatGPT today, Claude tomorrow, something else next year. Your connectors and governance rules stay put — only the client changes.

Security before convenience

Connectors are read-only until you say otherwise. Risky writes wait for a human. Credentials are encrypted and never shown twice.

Everything is attributable

Every tool call, credential change, and approval decision is recorded with who, what, when, and against which system.

How it works

Four steps to a working endpoint.

Most customers get from signup to their AI client calling a real business system in a single sitting.

Read the detail
1

Connect a system

Point a connector at your mailbox, API, or database. Enter credentials once — they are encrypted immediately and never displayed again.

2

Test before you expose

Run a connection test and see exactly which capabilities are available before anything is published to an AI.

3

Create an MCP server

Group connectors into a hosted endpoint scoped to one environment. Preview the generated tool catalog, then activate.

4

Connect your AI client

Issue a credential, paste the endpoint into ChatGPT, Claude, or your own client, and start working. Revoke it any time.

Built for the people who have to answer for it

Giving an AI access to real systems is an operational decision, not a demo. My MCPaaS is built around the controls that decision needs.

Roles that mean something

Owners, admins, builders, and viewers — with the boundaries enforced in the service layer, not just hidden in the UI.

Separate environments

Development and production are isolated: separate credentials, separate servers, separate audit trails.

Approvals on risky actions

Decide which tools can act on their own and which need a person to say yes first.

Health you can see

Scheduled connector checks, the last error surfaced in the portal, and a correlation ID on every failure.

Revocable credentials

Per-client tokens with expiry, rotation, and per-credential usage tracking.

Usage you can explain

Invocations by server, credential, and connector — so cost and value are visible before the invoice is.

Connect your first system today.

Start a 14-day trial, wire up a connector, and see your own AI client working against your real data before the day is out.