PipeStream MCP. Manage real-time data flow and events from your chat.

PipeStream connects your AI agent directly to high-throughput data streams, letting you manage event logging and real-time data flow with simple natural language commands. You don't need a whole separate monitoring suite; your agent handles it.

To start managing the system, remember that list_streams provides a complete list of every data channel currently running in PipeStream. Beyond just checking what's active, you can set up brand new channels using create_stream, defining both its name and how long it should keep records—you gotta specify that retention policy upfront.

When you need to log something instantly, publish_event sends a structured JSON payload immediately to any stream ID you point it at. This is what you use for logging critical events or sending data packets that need immediate processing. It’s super fast; you just give it the target and the data.

For historical deep dives, fetch_events pulls specific records from any existing stream. You don't have to manually sift through gigabytes of garbage; you can filter these past events by a precise date range or use pagination to walk through large datasets chunk by chunk.

Think about the process: first, your agent uses list_streams to see what channels are already operational. Then, if you need a new data source, it'll run create_stream to spin up a dedicated channel and set its lifespan policy—say, 90 days of retention. Once those streams are ready, you can immediately inject live activity.

To log an event, your agent just calls publish_event with the stream ID and the JSON payload; it's instant logging for auditing or processing purposes.

If you need to review what happened last Tuesday, instead of searching through a database GUI, you tell your AI client to run fetch_events. You specify the time frame—like 'August 1st to August 5th'—and the system pulls those exact historical records. If there are too many results for one page load, you can guide it using pagination parameters so you get a clean, manageable batch of data every time.

This whole loop means your agent doesn't just read; it actively controls the data flow. You list streams to check status, create streams to build capacity, publish events to write activity, and fetch events to read history. It’s a closed-loop system built right into your chat window. You keep all that visibility without ever leaving your prompt.

You're not just looking at logs; you're managing the entire pipeline lifecycle—from creation to real-time injection and deep historical retrieval.