Your live dashboard stops updating. Your chat application goes silent. Every HTTP check you have says the server is perfectly healthy.
The problem is a broken WebSocket connection — and standard HTTP checks will never see it. As modern application stacks have grown more complex, the definition of what a “synthetic check” needs to cover has expanded dramatically. HTTP is no longer enough.
This post covers three protocol categories that sit outside HTTP but are critical to the health of modern applications: persistent connections, data stores, and AI service endpoints.
Start Here: What the Videos Cover
▶ Video 8 — “WebSocket & Real-Time Protocol Monitoring”
▶ Video 9 — “The Rise of New Protocols — MCP and Database Monitoring”
WebSocket: The Protocol HTTP Checks Miss
HTTP follows a request-response pattern: open a connection, send a request, get a response, close. Every interaction requires a new connection. It’s stateless by design.
WebSocket is fundamentally different. It starts as an HTTP upgrade request — the client asks the server to switch protocols — and then becomes a persistent, bidirectional connection. Both sides can send data at any time, without opening new connections. This is what powers live dashboards, real-time collaborative editors, multiplayer games, and financial data feeds.
What a synthetic WebSocket check measures
Metric | What It Catches |
|---|---|
Connection time | How fast the HTTP upgrade handshake completes |
Message round-trip | Send a message, measure time to response — latency on the live connection |
Throughput | Data rate on the persistent connection under load |
Ping-pong keep-alive | Whether the connection stays alive between messages |
Failure modes unique to WebSocket
WebSocket failures are structurally different from HTTP failures. Connections can drop mid-stream — silently, without an error code the client can easily detect. Messages can arrive out of order or with high latency while the connection appears healthy. The upgrade handshake itself can fail, forcing the application to fall back to HTTP long-polling — which is slower, more expensive, and often invisible in your monitoring.
If your application uses live connections — and most modern applications do — regular HTTP checks are not a substitute. You need protocol-specific monitoring that speaks WebSocket natively and can distinguish between a broken connection and a slow one.
Database Monitoring: Reachable Isn’t the Same as Healthy
Your database server passes ICMP. TCP to port 5432 connects cleanly. From a connectivity standpoint, everything looks fine.
But the database is in a degraded state. Replication lag has grown to 45 seconds. A long-running query is holding a table lock. Memory pressure is causing the query planner to make poor decisions. None of this shows up in a connectivity check — because connectivity checks don’t run queries.
What a synthetic database check actually does
A meaningful database check goes beyond TCP port validation. It executes an actual query, measures execution time, validates the result, and checks replication status. The difference is the difference between asking “is the door unlocked?” and asking “is the building actually functional?”
Check Type | What It Validates |
|---|---|
Query execution time | Is the database responding within acceptable latency bounds? |
Replication lag | Are replicas keeping up with the primary? Lag affects read consistency. |
Connection pool health | Are connections available, or is the pool exhausted? |
Result validation | Does the query return expected data? Detects silent corruption. |
Why This Matters in Practice
A read replica with 45 seconds of replication lag looks healthy to every connectivity check. But users querying that replica are seeing data that’s nearly a minute stale. In an e-commerce context, that’s orders that appear to not exist. In a financial context, it’s account balances that don’t reflect recent transactions. The only way to catch this is to check replication status directly — not the port.
AI Service Monitoring: The Newest Frontier
Five years ago, synthetic monitoring meant HTTP checks and ping. Today, a growing portion of application logic runs inside large language models — and those models need to be monitored with the same rigor as any other production dependency.
The Model Context Protocol — MCP — defines how applications communicate with AI language models. It establishes a standard for how a client sends prompts, how the model invokes tools, and how structured responses are returned. As MCP becomes the standard interface layer for AI-integrated applications, it also becomes a new category of synthetic check.
What a synthetic MCP check validates
Validation | What Failure Indicates |
|---|---|
Prompt receipt | Model endpoint is reachable and accepting requests |
Tool invocation | Model can successfully call configured tools and functions |
Structured response | Output format conforms to expected schema — not just that a response arrived |
Response latency | Time to first token and total completion time within SLA bounds |
Drift detection | Response quality or behavior has changed from a validated baseline |
The last item — drift detection — is unique to AI service monitoring. An HTTP endpoint either responds correctly or it doesn’t. An LLM endpoint can respond successfully while producing outputs that have drifted from expected behavior due to model updates, context window issues, or tool misconfiguration. Detecting this requires checking response content, not just response status.
Synthetic monitoring evolves alongside application architecture. As your stack adds databases, caches, message queues, and AI services, your monitoring must follow — because port checks alone will never tell you if the service is actually working.
The Common Thread
WebSockets, databases, and AI services have different protocols and different failure modes, but they share a single underlying principle: connectivity is not the same as correctness.
A WebSocket connection can be established while silently dropping messages. A database can be reachable while its replicas fall behind. An AI service can return HTTP 200 while producing degraded output. In each case, a connectivity check gives you a false green. Only a protocol-aware check that actually exercises the service catches the real state.
This is what distinguishes mature synthetic monitoring from basic uptime checking. You’re not asking “is the host alive?” You’re asking “is the service working — end to end, right now, the way my users depend on it?”
Next in the Series
Part 5 — The Standards Behind the Practice: RFC 2544, TWAMP, and Traceroute. The formal methodologies that give synthetic testing its rigor.
Synthetic MonitoringWebSocket MonitoringDatabase MonitoringAI Service MonitoringMCPLLM ObservabilityInfrastructure MonitoringSRE
About Parlon
Parlon is an infrastructure observability platform built for enterprise teams operating complex, hybrid environments. Parlon combines active synthetic validation, real-time telemetry normalization, and learning-based alerting into a single platform — shifting operations from firefighting to foresight. Learn more at parlon.io.