Hevo Data MCP. Manage data pipelines and usage metrics conversationally.
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Hevo Data (ETL & Data Pipeline) MCP Server lets your AI client manage and audit your data flow. List all running pipelines, check destination status (BigQuery, Snowflake, Redshift), and monitor row usage to keep your data stack running smoothly.
Stop jumping between dashboards; get real-time pipeline status and usage metrics conversationally.
What your AI agents can do
Get pipeline
Retrieves detailed status information for a specific Hevo data pipeline.
Get usage
Reports your current account usage metrics and consumption against billing quotas.
List destinations
Lists all configured data warehouse destinations receiving replicated data.
Retrieves a list of every configured ETL pipeline running through Hevo Data.
Pulls deep details for one pipeline, including its status and mapping definitions.
Shows every warehouse (like BigQuery or Snowflake) that is currently receiving replicated data.
Retrieves a list of all defined data models and transformations attached to your pipelines.
Identifies and lists the high-level workflows (DAGs) that connect multiple data steps together.
Gets real-time metrics on your data row usage and overall account billing ceilings.
Ask AI about this MCP
Supported MCP Clients
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Hevo Data MCP Server: 6 Tools for Data Pipelines
These tools let your agent interact with your Hevo Data account. You can list pipelines, check usage, and map complex data flows without leaving your chat.
019d75b0get pipeline
Retrieves detailed status information for a specific Hevo data pipeline.
019d75b0get usage
Reports your current account usage metrics and consumption against billing quotas.
019d75b0list destinations
Lists all configured data warehouse destinations receiving replicated data.
019d75b0list models
Lists all defined data transformation models used in your pipelines.
019d75b0list pipelines
Retrieves a complete list of all active and inactive data pipelines.
019d75b0list workflows
Lists the high-level workflow orchestration graphs connecting multiple data steps.
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 Hevo Data (ETL & Data Pipeline), then connect any of our 4,700+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 4,700+ 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
What you can do with this MCP connector
Hevo Data's MCP Server lets your AI client manage and audit your data flow. You can list every active and inactive ETL pipeline using list_pipelines, and you'll get detailed status information for a specific pipeline with get_pipeline. You can list every configured data warehouse destination receiving replicated data with list_destinations.
You'll see all defined data transformation models used in your pipelines by calling list_models. To track high-level data flows, use list_workflows to get a list of all workflow orchestrations. You'll check your current account usage and billing limits, including row usage, by calling get_usage.
How Hevo Data MCP Works
- 1 Subscribe to the server and enter your Hevo Data API Key and Region.
- 2 Your AI agent receives your natural language prompt (e.g., 'What's my current usage?').
- 3 The agent calls the appropriate tool (e.g.,
get_usage) and sends the structured result back to you.
The bottom line is, you talk to your agent, and it talks to your data infrastructure.
Who Is Hevo Data MCP For?
Data Engineers, Analytics Leads, and Operations Managers. You're the person who wakes up at 2 a.m. when a dashboard breaks because a data pipeline failed, and you're tired of clicking through three different monitoring dashboards to find the root cause. This gives you a single chat interface for the whole data stack.
Uses the server to monitor ETL pipeline health, check destination replication status, and confirm data flow without leaving their terminal.
Checks transformation models and workflow orchestrations to verify data quality and ensure data is ready for reporting before a major presentation.
Tracks row usage and account billing ceilings to make sure the data pipelines stay within the defined organizational budget.
What Changes When You Connect
- See pipeline status and details instantly. Instead of going to the pipeline dashboard, ask your agent to use
get_pipelineto get the status of a specific flow. - Track resource consumption without logging into billing. Use
get_usageto check your row quotas and see how much data you've processed this month. - Verify data routing and targets. Running
list_destinationsshows you every warehouse (like Snowflake or BigQuery) where data is currently landing. - Map out complex data logic.
list_workflowshelps you see the entire data lineage map, showing how transformations connect across your whole stack. - Confirm data quality logic. Use
list_modelsto check the attached mappings and transformations, ensuring your staging logic is sound before reporting. - Get a full pipeline inventory.
list_pipelinesgives you a quick rundown of every data sync running, helping you know what's active and what's not.
Real-World Use Cases
Investigating a broken sync
A data engineer notices a dashboard is wrong. They ask their agent to run list_pipelines to see all flows. They narrow it down to the suspicious pipe, then use get_pipeline to check the failure logs and determine if the destination is the issue, running list_destinations next. The problem is solved in a chat, not four dashboards.
Budget check before migration
An operations manager needs to move a data source but doesn't want to overspend. They ask their agent to run get_usage. The agent reports the current row quota usage. This stops them from starting a migration that would blow the monthly budget.
Auditing data dependencies
An analytics lead needs to know every piece of data that feeds into the Q3 report. They ask the agent to run list_workflows and list_models. This instantly maps the entire dependency graph, confirming data integrity before the report goes out.
Onboarding a new team member
A new data team member needs to know what data sources exist. They ask the agent to run list_pipelines and list_destinations. The agent gives a clear, comprehensive list, skipping the manual step of navigating the Hevo UI.
The Tradeoffs
Checking status manually
Jumping between the Hevo web dashboard, the BigQuery console, and the billing portal to figure out if data synced, where it went, and if you overspent.
→
Ask your agent to run list_pipelines for an inventory, then get_pipeline for status, and finally get_usage to check the budget. It keeps everything in one chat window.
Assuming data quality
Running a pipeline because the source data changed, without checking if the necessary transformation models are still attached or if the workflow needs updating.
→
First, run list_models to validate the attached transformations. Then, check list_workflows to ensure the entire process flow accounts for the change. This prevents bad data from getting into production.
Missing the full picture
Only checking if the pipeline ran successfully, but missing if the destination warehouse was actually ready to receive the data.
→
Always check list_destinations first. This verifies the target system is live and accepting data before you even ask the agent to check the pipeline status.
When It Fits, When It Doesn't
Use this if your job requires knowing the status or structure of complex data flows. Specifically, if you need to list all active pipelines (list_pipelines), check a single flow's status (get_pipeline), or audit your spending (get_usage).
Don't use this if you just need to look at raw data in a spreadsheet. This tool is for infrastructure monitoring, not data querying. If you only need to know which data sources exist, use the list_destinations tool. If you need to see the full dependency graph, use list_workflows. If you need to check the overall data structure, use list_models.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Hevo Data. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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Works with 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 server provides 6 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Monitoring data pipelines usually involves a painful, multi-tab process.
Right now, figuring out if your data sync is running correctly means logging into the Hevo dashboard. You click the pipeline, check the status, then maybe open the destination warehouse console (BigQuery) to confirm the data landed. If you need to know how much data moved, you have to switch to a separate billing tab. It’s a lot of clicking and context switching.
With this MCP server, you just ask your agent. You say, 'Show me the status of the Stripe to BigQuery pipeline and how many rows it used.' The agent calls `get_pipeline` and `get_usage` and gives you the answer right here. You get the status and the metrics without leaving the chat.
Use `list_workflows` to map your data dependencies.
Manually tracing data lineage is a nightmare. You have to open the workflow map, click through multiple stages, and try to piece together which transformations feed which destinations. It’s slow, and you often miss the connections between stages.
Now, ask your agent to run `list_workflows`. It pulls the entire orchestration map and shows you every connection point instantly. You get the full, accurate picture of your data stack, every time.
Common Questions About Hevo Data MCP
How do I use `list_pipelines` to see all my data flows? +
Running list_pipelines gives you an immediate inventory of every data sync configured in Hevo. You'll get a list of all active and inactive pipelines, helping you know what's running across your whole stack.
Can I check my data usage with `get_usage`? +
Yes. get_usage pulls your real-time account metrics. You see how many rows you've used and what your remaining monthly quota is, helping you monitor costs.
What does `list_destinations` tell me? +
Running list_destinations lists all the external data warehouses—like Snowflake or BigQuery—that are set up to receive data from Hevo. It confirms your data targets are active.
Is `get_pipeline` better than checking the dashboard? +
Yes. get_pipeline pulls the specific status for one flow directly to your chat. It's faster and better for quick diagnostics than navigating the main dashboard.
How do I check what transformations are attached to a specific pipeline using `get_pipeline`? +
The get_pipeline tool shows the exact transformation mappings attached to a pipeline. This lets you confirm the staging logic and data quality rules without opening the Hevo dashboard.
If my data sync fails, how can I use `list_workflows` to find the root cause? +
list_workflows displays the DAG workflows that connect transformations. This helps you trace the data path and identify which specific connection point is causing the failure.
How do I manage billing and track my row usage limits with `get_usage`? +
The get_usage tool provides your account's current row replication metrics against your monthly quota. It also lets you see usage broken down by specific pipelines.
Do I need to manually configure my API key every time I use `list_destinations`? +
No, once you connect your Hevo Data API Key and Region, the server handles authentication. You just need to call the tool, and it connects automatically.
Can I check the status of my data destinations through my agent? +
Yes. Use the list_destinations tool to see all your warehouse targets. Your agent will provide the status and details of where your data is being replicated, ensuring delivery to platforms like BigQuery or Snowflake.
How do I find a specific pipeline's configuration? +
Use the get_pipeline tool with a unique Pipeline ID to extract explicit routing mappings and ingestion frequencies. This is perfect for auditing specific ETL flows without manual searching.
Can I monitor my account's row usage through a conversation? +
Absolutely. The get_usage tool retrieves real-time account usage metrics and billing ceilings, helping you track how many rows have been replicated and ensure you stay within your plan's limits.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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