# PipeStream MCP for AI Agents AI Agent Connect

> PipeStream is an Connector that gives your AI agent direct control over real-time data pipelines. Use it to create streams, push JSON events, and pull historical logs without leaving your chat window. It turns your agent into a live monitor for high-throughput data flows.

## Overview
- **Category:** developer-tools
- **Price:** Free
- **Endpoint:** https://edge.vinkius.com/vk_preview_jDneWqNf0y6klnjmvBtb8xcp1NrmOfetRftWEg97/ai-agent-connect
- **Tags:** event-streaming, real-time-data, data-pipelines, logging, api-integration

## Description

Managing data pipelines usually means jumping between different dashboards, running CLI commands, and hunting through logs to see what's actually happening. PipeStream changes that by bringing your event streams directly into your conversation with your AI agent. Instead of manually checking if a service is sending the right data, you can just ask your agent to show you the last few events or tell it to spin up a new testing channel on the fly. It makes handling high-volume data feel like a conversation rather than a chore. You can manage retention policies, monitor active channels, and verify event delivery in real-time. Because it's hosted on the Vinkius marketplace, you can get this connected to your favorite tools like Cursor or Claude in minutes. It's about getting rid of the tab-switching and the mental overhead of keeping track of multiple data flows, letting you focus on the actual data instead of the plumbing.

## Tools

### create_stream
Set up a new data stream with custom retention policies for your project. Use this to build out your pipeline architecture on the fly.

### list_streams
View all your active data channels to see what's currently running. This helps you maintain a clear overview of your infrastructure.

### fetch_events
Pull a list of events from a stream using time filters or pagination. This lets you inspect specific historical data points quickly.

### publish_event
Send a JSON payload to a specific stream to trigger actions or log data. It's the quickest way to inject test data into your flow.

## Prompt Examples

**Prompt:** 
```
List all my active PipeStream channels.
```

**Response:** 
```
I've retrieved your active streams. You have 3 streams:

| Stream Name | ID |
| :--- | :--- |
| **user-signups** | st_1 |
| **system-logs** | st_2 |
| **payment-events** | st_3 |
```

**Prompt:** 
```
Create a new stream named 'production-alerts' with 48 hours retention.
```

**Response:** 
```
Successfully created the stream **production-alerts**. 

*   **Status:** Active
*   **Retention Policy:** 48 hours
*   **Assigned ID:** st_99
```

**Prompt:** 
```
Fetch the last 5 events from stream st_1.
```

**Response:** 
```
I've fetched the 5 most recent events from **user-signups**. They include payload data for user registrations from the last hour.

Would you like me to summarize the data?
```

## Capabilities

### Create new data channels
Spin up a new stream with specific retention rules.

### Push live events
Send JSON payloads to your active streams instantly.

### View active streams
See a list of all your current data channels.

### Pull historical data
Grab past events from any stream with filters.

### Monitor stream health
Check the status of your data flows in real-time.

## Use Cases

### Incident Response
A DevOps engineer asks the agent to fetch the last 50 events from a failing payment stream to find the error.

### Data Analysis
A scientist asks the agent to create a stream for experimental results and then pull the first 100 entries.

### QA Testing
A developer asks the agent to create a temporary test stream and publish a batch of dummy user signups.

### Log Auditing
An admin asks the agent to list all active streams to ensure they are following the company's retention policies.

## Benefits

- Stop context switching by using list_streams to see all your active channels without opening a browser.
- Speed up debugging by using fetch_events to pull specific logs directly into your conversation.
- Create test environments faster by using create_stream to spin up new channels with custom retention.
- Verify data integrity instantly by using publish_event to push test payloads and check delivery.
- Monitor infrastructure health in real-time without needing to navigate complex dashboard menus.

## How It Works

The bottom line is you get a chat-based command center for your entire event streaming infrastructure.

1. Connect your PipeStream API key to the Connector via the Vinkius dashboard.
2. Open your preferred AI client and describe the data action you need.
3. Receive live updates or historical logs directly in your chat window.

## Frequently Asked Questions

**Can PipeStream MCP help me debug production issues?**
Yes, you can use it to pull live event logs and check stream statuses instantly without leaving your chat.

**How do I create a new data stream using this?**
Just tell your agent to create a new stream and specify any retention rules you want to apply.

**Can I use PipeStream to see my active data channels?**
Yes, it can list every active stream you have so you can monitor your infrastructure in one place.

**Is it possible to send test data to a stream?**
You can use the tool to publish JSON events to any active stream for testing or verification.

**Does PipeStream MCP support historical data?**
Yes, it allows you to fetch past events from your streams with specific filters.

**How does this work with my current data pipelines?**
It connects directly to your PipeStream account so your agent can manage the data flow for you.

**Can I create a new data stream with a specific retention period?**
Yes! Use the `create_stream` tool. You can specify the name and the `retention_hours` to define how long data should be stored in that logical channel.

**How do I send a JSON payload to an existing stream?**
Use the `publish_event` tool. Provide the `stream_id` and your JSON `payload`. You can also optionally include a custom ISO8601 timestamp.

**Is it possible to filter events by time when fetching data?**
Absolutely. The `fetch_events` tool allows you to provide a `from_timestamp` to retrieve only the events recorded after a specific point in time.