PipeStream MCP for AI. Manage real-time data flow and events from your chat.
Gemini Works with every AI agent you already use
…and any MCP-compatible client
How this MCP server connects to your AI agent
PipeStream connects your AI agent directly to high-throughput data streams. You manage event logging and real-time data flow—creating new channels, publishing events instantly, or fetching historical records using natural language commands.
What AI agents can do with PipeStream Automation
Create stream
Sets up a brand new data channel, allowing you to define its name and how long it should keep records.
Fetch events
Pulls specific historical events from any existing stream. You can filter this by date range or paginate through results.
List streams
Provides a complete list of every data channel currently active in the system.
ListStreams shows every data channel currently running in PipeStream.
Use create_stream to set up a dedicated, named channel and define its required retention policy.
PublishEvent sends a structured JSON payload instantly to a specified stream ID.
FetchEvents retrieves past records from any stream, allowing you to filter by time or page through large datasets.
Ask an AI about this
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What AI agents can do with PipeStream: 4 Tools for Data Stream Management
These four tools let you manage the entire lifecycle of event data—from creating streams to fetching historical payloads.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using PipeStream on VinkiusCreate Stream
Sets up a brand new data channel, allowing you to define its name and how long it should keep records.
Fetch Events
Pulls specific historical events from any existing stream. You can filter this by...
List Streams
Provides a complete list of every data channel currently active in the system.
Publish Event
Sends an immediate, structured event payload to a designated stream ID for logging...
Security and governance baked right in.
Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.
Claude AI
Open Claude Settings
Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.
Add Custom Connector
Click the "+" button and select Add custom connector. Paste your Vinkius endpoint URL:
https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp
Replace [YOUR_TOKEN_HERE] with your token
from cloud.vinkius.com. For OAuth-protected servers, expand
Advanced settings to add credentials.
Start a conversation
Open a new chat. The PipeStream integration is available immediately — no restart needed.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with PipeStream, then connect any of our 5,100+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,100+ others, all in one place
- Add new capabilities to your AI anytime you want
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
VINKIUS INFRASTRUCTURE
Cloud Hosted
Managed infra
V8 Isolated
Sandboxed per request
Zero-Trust Proxy
No stored credentials
DLP Enforced
Policy on every call
GDPR Compliant
EU data residency
Token Compression
~60% cost reduction
Built on the Model Context Protocol (MCP) for Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This connection provides 4 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Sifting through logs used to mean opening five different tabs and copy-pasting timestamps., Solved with Vinkius AI Gateway
Today, finding out why a payment failed means logging into the dashboard, clicking 'View Logs,' finding the correct stream ID, exporting the CSV, then pasting that data into a spreadsheet just to filter by date. It’s tedious, and you lose context between tabs.
With PipeStream MCP Server, your agent handles all of it. You simply tell your client: 'Show me payment failures from yesterday.' The agent uses `fetch_events` behind the scenes, giving you structured data right where you are working.
PipeStream MCP Server gives you full control over event logging.
You don't just use a default log. You dictate the structure by running `create_stream`, setting precise retention rules, and defining exactly what data belongs in that stream for future analysis.
It moves your system from passive logging to active, managed event infrastructure. You control the schema and the lifecycle of every single piece of data.
What your AI can actually do with this
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.
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Here's how it actually works
The bottom line is: It lets your agent act as a direct interface to your entire logging and event infrastructure.
Subscribe to the PipeStream MCP Server and provide your API key.
Reference a tool (like list_streams) in your prompt, telling your agent what data you need.
Your AI client executes the necessary calls, giving you real-time stream status or historical event payloads directly.
Who is this actually for?
This is for the ops engineer who needs to check logs during an incident without spinning up a dashboard. It’s for the data scientist who needs to pull sample JSON payloads right into their analysis session. And it's for any backend developer tired of jumping between CLIs and web UIs.
Checks event logs and stream statuses during an incident response, confirming exactly when a service failed or recovered.
Publishes experimental results or fetches sample data sets for analysis directly within their chat session context.
Verifies event delivery across services and spins up temporary testing channels without leaving the development environment.
What Changes When You Connect
Stop jumping between dashboards. You can monitor live streams and check event logs using list_streams or fetch_events—all within one conversation.
Handle high-volume logging instantly. Use publish_event to inject structured JSON payloads into a stream without writing any code or hitting an API endpoint directly.
Control data lifecycle. When you use create_stream, you enforce custom retention policies, ensuring old logs don't bloat storage unnecessarily.
Speed up incident response. During a critical outage, quickly run fetch_events on the relevant stream ID to see exactly what happened and when it occurred.
Contextual data access. Pull sample or experimental data for analysis by running fetch_events, keeping those raw records right in your current working context.
See it in action
Debugging a payment failure
A user notices payments are failing sporadically. They ask their agent to run list_streams first, finding the 'payment-events' channel. Next, they tell the agent to use fetch_events on that stream for the last hour, filtering by 'failure' status. The resulting payload shows the exact data field missing from the request.
Launching a new microservice
A backend developer needs a dedicated testing channel for a new service component. They use create_stream to make 'testing-svc-v2' and set retention to 7 days. They then run a test payload using publish_event, confirming the event lands correctly before merging the code.
Analyzing system performance
The ops team suspects resource exhaustion. The agent runs list_streams to identify all channels, then uses fetch_events on 'system-logs', paginating through thousands of records to pinpoint a specific increase in error rates over the last 30 minutes.
Running an experiment
A data scientist wants to test a new feature's impact. They run create_stream for 'experiment-beta', and then use publish_event to send structured JSON records simulating the beta traffic, all without touching the production system.
The honest tradeoffs
Treating it like a simple message board
Just dumping text into the stream. The agent gets confused because you didn't specify structure or schema for the payload.
Always provide structured JSON when using publish_event. Your payloads must be machine-readable, even if they contain human-generated data. This keeps the logs usable by downstream systems.
Ignoring stream boundaries
Trying to fetch all events from 'user-signups' without a time filter, resulting in thousands of records and massive context overflow.
When using fetch_events, always include specific criteria—either a narrow date range or a pagination limit. Never assume the agent can pull everything.
Relying on synchronous feedback
Expecting that immediately after running publish_event, the event is visible in all consuming dashboards, which isn't guaranteed.
Remember this is an asynchronous logging system. Use fetch_events later to verify consumption status or use a dedicated monitoring tool for real-time alerts.
When It Fits, When It Doesn't
Use PipeStream if your core need is observing, logging, and interacting with high volumes of discrete events (e.g., user clicks, API calls, system state changes). It excels when you need to treat event data like a persistent log that can be queried later.
Don't use this if you are trying to run complex command-and-control orchestration—if your workflow requires multi-step conditional logic (e.g., 'If X happens, then run service Y and only if it succeeds, send Z'), look into a dedicated workflow engine tool instead. PipeStream is for the data flow itself; other tools handle the decision making.
Questions you might have
How do I check if a specific event was successfully logged using PipeStream? (pipe_stream) +
You use fetch_events. Simply specify the stream ID and the approximate time window. This returns the payload data, letting you confirm the record exists in the system.
Can I create a custom log channel with PipeStream? (pipe_stream) +
Yes, run create_stream. You must provide a name and specify the retention policy—this defines how long that stream will keep data automatically.
What should I use to send an event when my service calls it? (pipe_stream) +
Use publish_event. This tool takes a JSON payload and sends it immediately to the target stream ID, making sure the data is captured in the log.
Does PipeStream list all my available channels? (pipe_stream) +
Yes. Run list_streams. This gives you a definitive, current view of every active channel name and status within your account.
How do I authenticate when using the `publish_event` tool? +
You must include your unique PipeStream API key in the request header. This key authorizes your AI client to write data and events to any stream you manage.
Does `fetch_events` support retrieving data only within a specific date range? +
Yes, you can use time-based filters with the fetch_events tool. Just specify the start and end timestamps to narrow down your historical search results efficiently.
Can I set a data lifespan using the `create_stream` tool? +
Yes. When you run create_stream, you define the desired retention policy. This setting controls exactly how long PipeStream keeps event payloads before automatically removing them.
How do I check the current status or overall health of my data channels? +
The list_streams tool shows all active channels, but for real-time monitoring, you can inspect the live flow of data directly in your chat interface. This lets you track activity across all streams without switching tools.
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.
We've already built the connector for PipeStream. Just plug in your AI agents and start using Vinkius.
No hosting. No infrastructure. No complex setup.
All 4 tools are live and waiting.
You're up and running in seconds.
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