Remote MCP servers let users connect AI applications to your service without installing anything. Deploying them is like running any production web service, with a few specifics.
Hosting
Any platform that serves HTTP works: containers, serverless functions or existing application servers. Streaming responses require infrastructure that supports long-lived or streamed HTTP connections.
Authorisation
Implement OAuth-based authorisation as the specification describes, validate tokens on every request and apply the user's own permissions to every tool call.
Sessions and Scaling
The protocol can use session identifiers. If you run multiple instances, make sure sessions work across them — shared storage or stateless design.
Rate Limiting and Quotas
Models may call tools rapidly. Apply per-user rate limits and protect back-end systems.
Monitoring
Track requests, errors, latency and tool usage. Alert on unusual patterns that could indicate abuse or a misbehaving agent.
Multi-Tenancy
Ensure strict separation between customers' data in every tool and resource.
Versioning
Announce changes, keep tool names stable, and support the protocol versions your users' clients use.
Documentation
Tell users what the server does, which scopes it needs and how data is handled.