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No. MCP servers are not serverless by definition. The Model Context Protocol supports two deployment shapes: a client can launch a local server as a stdio subprocess, or it can call an independently hosted server over Streamable HTTP. “Serverless” describes where and how that HTTP service runs—such as a managed container or function platform—not the MCP protocol itself.
This distinction determines whether your server needs a local runtime, an HTTPS endpoint, shared state, session affinity, authentication, and controls for cold starts and scaling.
How MCP clients, hosts and servers fit together
An MCP host such as Claude, VS Code, Gemini CLI or Cursor contains an MCP client. The client connects to an MCP server that exposes tools, prompts and resources. The transport between client and server determines the deployment model.
- Local stdio: the host launches the server process on the same machine.
- Streamable HTTP: the server runs independently and receives JSON-RPC messages at an HTTP endpoint.
A single application can have local servers for desktop integrations and remote servers for shared, centrally governed tools. Those are different hosting choices, not different protocol families.
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What “serverless MCP” actually means
A serverless MCP deployment packages a Streamable HTTP server as a function or managed container. The platform provisions compute on demand, may scale instances horizontally and can reduce capacity when idle. A long-running virtual machine, Kubernetes workload or dedicated container can implement exactly the same HTTP transport without being serverless.
Cloud Run is an explicit example: its documented MCP support is for Streamable HTTP servers packaged from a container image or source. It does not host stdio MCP servers. The deployed service receives an HTTPS URL that MCP clients can call.
Can you use Cloud Run?
Yes, when your server implements Streamable HTTP. Package the server, configure the process to listen on the port supplied by the platform, deploy it, and protect the resulting HTTPS endpoint with authentication and authorization. A desktop client cannot ask Cloud Run to launch a local stdio subprocess; stdio requires process access on the client host.
What about Lambda or another function platform?
The protocol does not require a particular cloud vendor. A function platform can be used only if you provide an HTTP adapter that correctly handles MCP’s Streamable HTTP negotiation, JSON-RPC requests and optional streaming responses. The supplied deployment guidance establishes Cloud Run support, but does not establish a specific Lambda MCP integration, so verify the current runtime and streaming behavior before committing to it.
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Local stdio versus remote Streamable HTTP
| Characteristic | Local stdio | Streamable HTTP |
|---|---|---|
| Process ownership | The MCP client launches and supervises a subprocess. | An independent service process is deployed and operated separately. |
| Wire format | Newline-delimited JSON-RPC on standard input and output; logs should use standard error. | JSON-RPC over HTTP POST; a response may be a JSON object or an SSE stream. |
| Typical reach | One user account and machine, with local filesystem and runtime access. | Any authorized client that can reach the endpoint. |
| Scaling | Starts with each client; no shared remote fleet. | Can use ordinary HTTP load balancing and horizontal replicas. |
| Operational burden | Simple server setup, but every client needs the runtime and configuration. | Requires deployment, HTTPS, authentication, monitoring and network policy. |
| Best fit | Desktop IDE integrations, personal agents and local development. | Shared tools, governed enterprise access and centrally managed services. |
Do MCP servers need persistent sessions?
Not necessarily. The MCP release candidate announced on May 21, 2026 describes a stateless protocol core. Its design dated July 28, 2026 can sit behind a normal round-robin load balancer, route using an Mcp-Method header and avoid sticky sessions or a protocol-level shared session store.
Stateless protocol handling does not mean your application has no state. If a workflow spans several calls, store durable data in a database, object store or other backing service and pass an explicit state handle between calls. AWS describes this pattern for stateless MCP servers. Each request can then land on any healthy replica.
When stateful handling is still useful
Some implementations or older capabilities depend on session continuity. A managed service such as Amazon Bedrock AgentCore documents stateless mode as the default recommendation while retaining a stateful mode for session-dependent capabilities. Choose stateful operation only when the feature requires it, and understand that it can introduce affinity, recovery and failover concerns.
Deployment decision framework
| Option | Best fit | Constraints to plan for |
|---|---|---|
| Local stdio process | Desktop IDE or local agent integrations | Client must launch the process and provide its runtime and filesystem access. |
| Cloud Run | Remote Streamable HTTP server in a container or supported source deployment | Does not host stdio; configure the HTTP port, identity and endpoint protection. |
| Amazon Bedrock AgentCore | Managed AWS agent workloads | Streamable HTTP is required; select stateless or stateful behavior deliberately. |
| Google Cloud remote MCP services | Discoverable, governed remote tools | Plan HTTP authentication, IAM, discovery and optional toolsets. |
How to host a remote MCP server
The exact framework commands differ, but the production sequence is consistent.
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- Make the process platform-friendly. Read the platform-provided listening port, keep application logs out of the JSON-RPC response channel, and ensure the process can terminate and restart cleanly.
- Deploy behind HTTPS. Cloud Run or an equivalent managed service gives the client a stable HTTPS URL. Put a gateway or load balancer in front if you need centralized policy, rate limits or multiple services.
- Implement protocol negotiation. Send and validate the MCP HTTP headers required by the current specification. Handle POST requests, JSON responses and SSE responses according to the client capabilities you support.
- Authenticate every connection. Use your identity provider, signed credentials or an equivalent mechanism. Do not treat an obscure URL as authorization.
- Validate the Origin header. For incoming connections, reject invalid origins with HTTP 403 to reduce DNS-rebinding risk. Do not bind a local development server to
0.0.0.0; use127.0.0.1unless you intentionally need network access. - Define authorization per tool. Apply IAM or an equivalent policy layer so a caller can use only the tools and resources it is allowed to access. Treat both tool arguments and returned data as sensitive.
- Choose a state strategy. Keep requests stateless where possible. If a workflow needs continuity, persist data externally and return an explicit state identifier that later calls can present.
- Prepare for retries and duplicate calls. Network retries can repeat a request. Make mutating tools idempotent where practical, or attach an operation identifier so the application can detect duplicates.
- Add observability before launch. Record request IDs, MCP method names, authentication decisions, duration, response status and tool-level errors without logging secrets or personal data.
Scaling, streaming and reliability trade-offs
Horizontal scaling
A stateless request path lets a round-robin load balancer distribute calls across replicas. You do not need sticky sessions or a shared session store for protocol mechanics when the application passes state explicitly. Your backing database or queue can still become the scaling bottleneck, so size and monitor it independently.
Cold starts
On-demand platforms can introduce startup delay when no warm instance is available. Keep initialization lightweight, cache read-only configuration safely and set client timeouts that allow for a cold start plus normal request processing. If predictable latency matters more than idle cost, use minimum warm capacity or a long-running service.
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Streaming behavior
Streamable HTTP may return a complete JSON object or an SSE stream. Confirm that the chosen platform, proxy and authentication layer preserve streaming connections and do not buffer them until completion. If your tool calls are short and bounded, ordinary JSON responses simplify operations; long-running work may need streaming or an asynchronous task pattern.
Cost and control
Serverless billing generally follows requests, execution time and provisioned capacity, while containers and VMs trade a steadier baseline cost for more control. Compare expected traffic, idle periods, startup tolerance, network egress and operational staffing rather than assuming one model is always cheaper.
Security checklist for remote servers
- Allow only approved origins and return HTTP 403 for invalid ones.
- Authenticate every remote client and rotate credentials.
- Authorize tools and resources with IAM or an equivalent policy.
- Keep local-only servers on
127.0.0.1, not0.0.0.0. - Validate and constrain tool inputs; treat outputs as data that may require scanning. Google documents Model Armor scanning for MCP calls and responses.
- Keep secrets in a managed secret store, not in tool arguments, source code or logs.
- Set request, connection and idle timeouts that match tool behavior.
- Log enough metadata to investigate failures while redacting tokens and user content.
Common problems and fixes
“The client cannot start my server”
With stdio, verify the executable path, working directory, environment variables and runtime permissions on the client machine. Ensure the server writes only valid JSON-RPC to standard output; move diagnostics to standard error.
“Cloud Run deployment works, but the client gets no response”
Check that the process listens on the platform-provided port, that the client is using the HTTPS endpoint, and that your service supports Streamable HTTP rather than only stdio. Inspect gateway logs for blocked POST requests or buffered SSE responses.
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“Requests fail with HTTP 403”
Review both authentication and Origin validation. A valid token does not compensate for an origin your server has not allowed. Check the exact origin sent by the MCP client and update policy deliberately.
“A second request cannot find workflow state”
Your replica-local memory is not durable. Move state to a shared backing store and pass a state handle between calls, or intentionally select a stateful service mode with the associated session-management requirements.
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Assume a network retry or client replay is possible. Add idempotency keys or durable operation records for side-effecting tools, then return the original result when the same operation is received again.
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Can one MCP host use both transports?
Yes. A host can launch some configured servers locally over stdio while connecting to other servers through their HTTPS Streamable HTTP endpoints; each server entry specifies its own transport.
Does protocol statelessness eliminate databases?
No. It removes a requirement for protocol-session affinity, but applications still use databases or other stores when a workflow must survive multiple requests or replicas.
What should I verify before choosing a function platform?
Confirm support for long-lived HTTP connections, SSE streaming if required, request size and timeout limits, identity integration, and a way to persist application state outside the function instance.
The Bottom Line
MCP is a protocol, not a hosting model. Use stdio when the client should launch a local process; use Streamable HTTP for a remotely hosted service. Serverless platforms such as Cloud Run can run the latter, provided you design authentication, Origin checks, state handling, streaming and observability for a distributed system.
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