Do these 3 things before closing this tab:
1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsRun an MCP server like any other application service: implement tools with an official SDK or FastMCP, use stdio when a local client launches the process, and use Streamable HTTP when clients connect over a network. Then containerize the server, place HTTP deployments behind your normal TLS gateway, enforce Origin and host validation, authenticate every connection, and monitor the service as you would an API.
Choose the transport before you deploy
The transport determines how the client reaches your server and how much infrastructure you need.
| Transport | Best fit | How it works | Operational implications |
|---|---|---|---|
| stdio | A client and server on the same machine | The client launches the server as a subprocess. Messages are newline-delimited JSON-RPC on standard input and output. | No listening socket or reverse proxy is required. Standard output must contain only valid MCP messages; write diagnostics to standard error. |
| Streamable HTTP | Remote access, multiple clients, gateways, containers and managed HTTP platforms | One MCP endpoint accepts POST and GET. Responses can be JSON or Server-Sent Events. | Use normal TLS termination, authentication, rate limiting, load balancing and HTTP observability. |
| HTTP+SSE (legacy) | Older clients that have not migrated | Separate legacy SSE and POST endpoints. | Keep compatibility endpoints only for as long as clients require them, then remove them deliberately. |
The protocol semantics are intended to remain the same across bindings. Do not select stdio merely because it is easier to code if the client must run elsewhere; a subprocess is inherently local. Conversely, do not expose HTTP for a desktop integration that can safely run as a local process.
Implement a small server
Use a version-pinned official SDK or FastMCP. The following FastMCP example exposes one deterministic tool and supports both local and HTTP operation. Check the run options for the exact SDK version you pin.
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from fastmcp import FastMCP
mcp = FastMCP("infrastructure-demo")
@mcp.tool
def add(a: int, b: int) -> int:
"""Add two integers."""
return a + b
if __name__ == "__main__":
# For a local client launched through stdio:
mcp.run()
# For Streamable HTTP, run this entry point instead:
# mcp.run(transport="streamable-http", host="0.0.0.0", port=8000)
Keep the stdio and HTTP launch modes as separate deployment commands rather than trying to serve both from one process. For stdio, never print banners, logs or tracebacks to stdout. Send them to stderr or a file so the client receives an uncorrupted JSON-RPC stream.
Define the configuration contract
- Read credentials and downstream URLs from environment variables or a secret manager, not from source code.
- Make the listening host, port, log level and allowed origins configurable.
- Expose a health check that does not call a privileged downstream tool. A process or TCP check is safer than making a health probe perform real work.
- Return structured errors and stable tool names. Changing a tool name is a client compatibility change.
Run it locally with stdio
- Create a virtual environment and install the pinned SDK version listed in your lock file.
- Start the server with the client’s MCP configuration, pointing its command and arguments at your entry-point file.
- Confirm that the client completes the initialize handshake and reports the protocol version it negotiated.
- Exercise every tool with non-production credentials before connecting real systems.
If you test the process manually, remember that stdio is a protocol stream, not an interactive shell. Use an MCP-capable client or a protocol test harness; typing arbitrary text into the process will not produce a valid request.
Expose the server with Streamable HTTP
For a remote deployment, bind the application to the container interface and let your gateway provide the public address. A typical start command is:
python server.py --transport streamable-http --host 0.0.0.0 --port 8000If your SDK uses code-based configuration instead of command-line options, set the equivalent Streamable HTTP transport, host and port in the entry point. The MCP endpoint must be the path registered with your client; do not assume that a generic application route is an MCP endpoint.
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FROM python:3.12-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir --requirement requirements.txt
COPY server.py .
EXPOSE 8000
CMD ["python", "server.py", "--transport", "streamable-http", "--host", "0.0.0.0", "--port", "8000"]Build and run it with:
docker build -t mcp-demo:1.0.0 .
docker run --rm -p 8000:8000 --env-file .env mcp-demo:1.0.0Pin the base image, runtime and dependencies, and rebuild when security updates are available. Docker gives you repeatable packaging and isolation; it does not provide authentication or tool-level authorization.
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Deploy on a VM or managed container service
- Run the container under a supervisor with automatic restart and resource limits.
- Put it behind your existing reverse proxy or load balancer. Terminate TLS there, forward only the MCP route, and disable direct public access to the container port.
- Configure the accepted hostname and Origin values explicitly. A host allowlist that omits the deployed hostname can make the server reject every request.
- Restrict outbound traffic to the APIs the tools actually need.
- Register the HTTPS MCP endpoint in the client and repeat the initialize and tool tests through the gateway, not only against localhost.
Deploy on Kubernetes
A minimal deployment uses one container port and a TCP readiness probe; add your organization’s ingress, identity and network-policy resources separately.
apiVersion: apps/v1
kind: Deployment
metadata:
name: mcp-demo
spec:
replicas: 2
selector:
matchLabels:
app: mcp-demo
template:
metadata:
labels:
app: mcp-demo
spec:
containers:
- name: mcp
image: registry.example.com/mcp-demo:1.0.0
ports:
- name: http
containerPort: 8000
readinessProbe:
tcpSocket:
port: http
periodSeconds: 10
resources:
requests:
cpu: 100m
memory: 128Mi
limits:
cpu: 1
memory: 512Mi
---
apiVersion: v1
kind: Service
metadata:
name: mcp-demo
spec:
selector:
app: mcp-demo
ports:
- name: http
port: 8000
targetPort: httpUse an ingress or gateway for TLS, authentication and the public hostname. Add a NetworkPolicy that permits only required ingress and egress. Store API keys in a Kubernetes Secret or an external secret manager, and rotate them without rebuilding the image.
Use a serverless HTTP platform
Streamable HTTP can run on a managed HTTP platform when the platform supports the required request and response behavior. Google Cloud’s deployment guidance documents this pattern for Cloud Run. Verify streaming, request timeouts, maximum body sizes and connection handling on your chosen platform before production rollout.
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Secure every exposed endpoint
The MCP specification requires servers to validate the Origin header on every incoming connection to prevent DNS rebinding attacks. Treat this as a mandatory control, not an optional browser feature.
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- Bind safely: local servers should listen on
127.0.0.1, not0.0.0.0. A remotely reachable server should bind inside its private network and be exposed only through the intended gateway. - Authenticate all connections: use OAuth or another strong identity layer suitable for the clients you support. Do not rely on an obscure URL, network location or an API key embedded in client-side code.
- Allow only known hosts and origins: configure an explicit allowlist and test the exact production hostname, including any gateway-generated host.
- Authorize each tool: identity proves who connected; tool authorization decides what that identity may do. Give downstream credentials the least privilege needed for each tool.
- Limit abuse: apply per-identity and global rate limits, request-size limits and sensible timeouts.
- Protect secrets: use a secret manager, redact credentials from logs, and define a key-rotation procedure.
- Constrain egress: allow calls only to required downstream services. This reduces the impact of a malicious prompt or compromised credential.
- Audit: log authentication failures, tool name, caller identity, latency, outcome and a correlation ID. Never log secret values or sensitive tool arguments.
Scale without losing state
The 2026-07-28 release candidate describes a stateless core designed to scale on ordinary HTTP infrastructure. With stateless request handling, a load balancer can distribute calls across instances without hidden transport session affinity, provided durable state is externalized and any required continuation handle is carried in protocol data.
That design does not make every client stateless. Confirm whether each installed client expects sessions, GET-based SSE, DELETE teardown or the newer stateless request model. The same release candidate adds MCP method and name headers that can help gateways route requests and apply method-specific rate limits.
Externalize durable state
- Store user configuration, job records and continuation data in a database or durable object store.
- Keep instances interchangeable; a request sent to a different replica must still find the required state.
- Do not use process memory as the source of truth for authorization, long-running jobs or resumable operations.
- Set client and gateway timeouts based on the slowest legitimate tool, not on an arbitrary web default.
Plan a compatibility window
During migration, you may need to keep legacy SSE and POST endpoints alongside the newer MCP endpoint. Measure which clients still use them, publish a removal date, and test the upgrade path before deleting compatibility routes. Do not assume that a newly installed client has adopted the latest protocol behavior.
Observe and operate the service
At minimum, collect:
- Request and tool-call count, latency percentiles and error rate.
- Authentication failures, rejected Origins and rejected hosts.
- CPU, memory, network and container restarts.
- Downstream API latency, quotas and failures.
- Per-tool volume and authorization denials.
Add a liveness check for process health and a readiness check for whether the instance can accept traffic. Keep health probes cheap and unauthenticated only when they reveal no sensitive information. Define alerts, an emergency disable switch for dangerous tools, a rollback procedure and a key-rotation runbook before launch.
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Performance, reliability and cost decisions
- Latency: stdio avoids network hops for local clients. HTTP adds gateway, TLS and authentication overhead but enables shared infrastructure and remote access.
- Throughput: scale HTTP replicas horizontally once tool execution, database connections and downstream quotas—not merely CPU—are measured.
- Reliability: use bounded timeouts, retries only for idempotent downstream operations, graceful shutdown and readiness draining during deployments.
- Cost: compare the operational burden of a VM, Kubernetes or a managed container platform. Include gateway, logging, egress, database and identity costs, not just container CPU.
- Isolation: separate high-risk tools or tenants when a shared process would grant excessive network or credential access.
Troubleshoot common failures
| Symptom | Likely cause | Fix |
|---|---|---|
| Client reports invalid JSON or hangs on startup | Logs or a banner were written to stdout in stdio mode. | Send diagnostics to stderr, ensure each message is newline-delimited JSON-RPC, and remove shell wrappers that echo text. |
| Every HTTP request is rejected | Origin or host allowlist does not include the actual client or gateway hostname. | Inspect the received headers, add only the exact trusted values, and redeploy. Do not disable validation globally. |
| 401 or 403 before a tool runs | Missing identity, expired token or tool-level permission. | Verify the client’s credential flow, token audience and scopes, then test with a least-privilege account. |
| Works locally but times out through the gateway | TLS route, proxy timeout, streaming behavior or network egress is wrong. | Test the public URL, increase only the required timeout, confirm POST/GET handling and inspect gateway logs. |
| Requests fail after adding replicas | Session or continuation data is stored only in process memory. | Externalize durable state and confirm whether the client requires affinity during the migration period. |
| Older client cannot connect to the new endpoint | It expects legacy HTTP+SSE behavior or a different protocol version. | Keep compatibility endpoints temporarily, document the supported version, and upgrade the client before removal. |
| Tools can reach too many systems | Broad service credentials or unrestricted container egress. | Replace credentials with per-tool least-privilege identities and enforce outbound network policy. |
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cURL
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The Tool Desk
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Can I put an MCP server behind an ordinary reverse proxy?
Yes. Streamable HTTP is designed to use standard HTTP infrastructure; configure the proxy to preserve the MCP endpoint’s POST and GET behavior, TLS, authentication headers and any required streaming responses.
Should a production MCP server run in one container or several?
Start with one well-observed instance when traffic is small, then add replicas after moving durable state outside the process and verifying that the client does not require session affinity.
Is Docker enough to secure an MCP server?
No. Containers package and isolate the process, but you still need Origin and host validation, authentication, authorization, secret management, network controls and audit logs.
How do I remove a dangerous tool quickly?
Use a deployment or feature flag that disables the tool, revoke its downstream credential, and verify through the client that authorization failures are logged and no calls reach the downstream system.
Quick Recap
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