Stitch Data MCP for AI. Manage all ETL pipelines from chat, not dashboards.
Gemini Works with every AI agent you already use
…and any MCP-compatible client
How this MCP server connects to your AI agent
Stitch Data connects your data warehouse infrastructure directly to any AI agent. It lets you manage entire ETL pipelines—from listing sources and configuring destinations (like Snowflake or Redshift) to running manual syncs and pushing custom data batches—all through natural conversation.
What AI agents can do with Stitch Data Automation
Create account
Creates a new Stitch client account (only for partners).
Create destination
Sets up and configures a new data destination for the account.
Create ephemeral session
Generates a temporary token needed for front-end client connections.
Create and delete data sources and destinations (like S3 or Snowflake) directly through the server.
Manually start a replication job to keep your warehouse up-to-date with source changes.
Push specific groups of records for a table, ensuring best data typing and high integrity loading.
List recent extraction jobs or check the overall import status to verify if your pipelines are running correctly.
Control which specific tables and fields from a source are selected for replication.
Ask an AI about this
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What AI agents can do with Stitch Data: 21 Tools for Data Pipelines
These tools let you programmatically control every step of your data workflow—from creating sources to pushing batched records into your warehouse.
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 Stitch Data on VinkiusCreate Account
Creates a new Stitch client account (only for partners).
Create Destination
Sets up and configures a new data destination for the account.
Create Ephemeral Session
Generates a temporary token needed for front-end client connections.
Create Source
Registers and creates an entirely new data source within the system.
Delete Destination
Removes a configured destination from the account list.
Delete Source
Permanently removes an existing data source connection.
Get Import Status
Checks if the Stitch Import API is currently operational and running correctly.
List Destination Types
Shows a list of all supported destination types, like Redshift or Snowflake.
List Destinations
Lists all the data destinations currently configured for your account.
List Extractions
Retrieves a list of recent job attempts to pull data from sources.
List Loads
Displays records of past attempts to load data into your destination warehouse.
List Source Types
Shows all types of sources that Stitch can connect to.
List Sources
Lists every data source currently connected and configured for your account.
List Streams
Shows all tables (streams) available within a specific, selected source.
Push Import Batch
Sends a controlled batch of records for one table to the Import API using schema...
Push Import Data
Loads raw data for one or more tables without enforcing strict schemas.
Start Sync
Manually triggers a full replication job to update the data warehouse from a source.
Update Destination
Modifies the settings or credentials of an existing destination connection.
Update Source
Changes settings, pauses, or unpauses a connected data source.
Update Stream Metadata
Selects specific tables and fields that should be included in the replication...
Validate Import Data
Checks if your credentials and data format are correct without saving any records.
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 Stitch Data 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 Stitch Data, 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
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Stitch 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.
VINKIUS INFRASTRUCTURE
Cloud Hosted
Managed infra
V8 Isolated
Sandboxed per request
Zero-Trust Proxy
No stored credentials
DLP Enforced
Policy on every call
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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 21 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Debugging data pipelines used to mean logging into a dozen different web portals., Solved with Vinkius AI Gateway
Today, if your sync fails, you have to jump through hoops. You check the source's dashboard for credentials; then you go to the destination portal to see connection status; after that, you hit a third monitoring page just to look at the extraction logs. It’s copy-pasting IDs and switching tabs until you find the root cause.
With this MCP server, all those steps collapse into one conversation. You ask your agent to check the sync health using `get_import_status`, and it gives you the status right here. You get instant visibility on pipeline failures without opening a single browser tab.
Stitch Data MCP Server: Control Every Move
The biggest time-saver is running `start_sync`. Instead of waiting for the nightly cron job to run and hoping it works, you can trigger a manual replication job at 10 AM sharp. This gives you immediate control over data freshness.
You own the pipeline now. You manage sources with `update_source`, you confirm destinations with `list_destinations`, and you load custom tests using `push_import_batch`. It’s direct, controlled, and entirely conversational.
What your AI can actually do with this
You're using Stitch Data to manage your ETL pipelines, and this server gives your AI client full control over everything—from setting up connections to running custom data loads. You don't have to leave your chat window to handle complex data workflows.
Managing Connections: Sources and Destinations
You can start by listing all the types of sources Stitch supports using list_source_types, or see every source currently connected through list_sources. If you need to add a new connection, you use create_source to register it. You can also delete connections permanently with delete_source. On the destination side, you check which types are supported via list_destination_types, then view all configured endpoints using list_destinations.
To set up a new spot for your data, run create_destination. If that destination changes or gets retired, you use update_destination to modify it, or delete_destination to wipe it clean.
Controlling Data Flow and Replication Jobs
When it comes to moving data, you've got multiple options. You can manually kick off a full data refresh job using start_sync, which keeps your warehouse current with the source changes. For more control over what moves, you use update_stream_metadata to select specific tables and fields that need replication.
If you wanna update an existing connection or pause it, run update_source. You'll also find list_streams lets you see all the individual tables—the streams—that exist inside a source you’ve selected.
Loading Custom Batches and Data Integrity
Sometimes, you don't want a full sync; you just need to push specific data. If you send a controlled batch of records for one table, using push_import_batch forces schema validation, which keeps your data high integrity before it lands. When you know the schemas are solid and just need to dump raw data into multiple tables without strict rules, use push_import_data.
For partners who manage client accounts, you can create a new Stitch client account with create_account, or if your front-end client needs temporary access, you generate a token using create_ephemeral_session.
Monitoring and Maintenance Checks
How do you know if this thing is running right? You check the overall health of the import process using get_import_status. To look back at what ran, you use list_extractions to view a list of recent attempts to pull data from sources, or list_loads for records detailing past load attempts into your destination warehouse.
Before you commit any actual data, run validate_import_data; this checks if your credentials and data format are correct without saving anything. Finally, when you're done setting up the whole thing, you can still check which destinations are set up via list_destinations or what types of sources you've got connected with list_sources.
You just talk to the agent, and it handles all this heavy lifting for ya.
019ea609-10ab-7373-b879-b799b4d8d765 
Here's how it actually works
The bottom line is you manage your entire data flow by talking to it; no dashboards or clicks required.
Subscribe to the server and provide your Stitch Connect Token, Import Token, and Region.
Use commands like list_sources or create_destination to define where your data is coming from and going.
Tell your AI agent what to do—for example, 'Start a manual sync for Source ID ABC'—and watch the status update in the chat.
Who is this actually for?
This tool is for people whose job involves moving, verifying, and maintaining massive amounts of structured data. If you're a Data Engineer dealing with broken syncs at 2 AM, or an Analytics Team who needs to quickly test ad-hoc reports by dumping custom records into Snowflake, this saves time.
You run list_extractions and get_import_status to check sync health. You use update_source when a source API changes or needs to be paused.
When you need to test reporting on new data, you run push_import_batch, sending specific record sets directly to the warehouse for immediate validation.
You monitor pipeline health across multiple client accounts by running list_destinations and verifying successful loads using list_loads.
What Changes When You Connect
Check sync status immediately. Instead of logging into the web UI to see if a job ran, just run list_extractions or get_import_status and know what's up.
Handle schema changes easily. You can use update_stream_metadata to tell the system exactly which fields you want replicated without touching the source API.
Load data when needed. Use push_import_batch for custom, high-integrity testing loads into your warehouse, bypassing scheduled syncs entirely.
Manage connections in one go. You can list all sources with list_sources, then use update_source to pause or restart any connection without leaving the chat window.
Audit everything. Run list_loads to see a history of every attempt to move data, helping you pinpoint when and where data went wrong.
See it in action
Debugging a Broken Sync
The Data Engineer notices the Snowflake warehouse is empty. Instead of clicking through three different monitoring dashboards, they ask their agent to run list_extractions and then check the logs using get_import_status. The agent reports that the source connection failed due to bad credentials, allowing an immediate fix via update_source.
Ad-Hoc Reporting Test
A Product Manager needs to test a new report using user data from last night. They can't wait for the next scheduled sync. They use their agent to run push_import_batch with the raw records, loading them directly into Redshift for instant validation.
Onboarding New Data Sources
A team needs to connect a new CRM system. Instead of manually configuring everything on the web portal, they use their agent to first run list_source_types to confirm compatibility, then create_source, and finally start_sync in sequence.
Auditing Data Flow
The Analytics team suspects data might be missing. They instruct their agent to run list_destinations first to ensure the target is correct, then use list_loads to track all recent transfer attempts and verify the count of records.
The honest tradeoffs
Assuming Data Integrity
Running a sync job (start_sync) without first checking if your data is formatted right, leading to corrupted or mis-typed fields in the warehouse.
Always validate before loading. Run validate_import_data first. This tests credentials and format against the request structure without writing any bad data.
Overloading the System
Trying to dump massive, unsorted datasets into a table using general tools like push_import_data, which doesn't enforce schemas and might corrupt downstream reports.
Use structured loading. Always prefer running push_import_batch when you know the schema, as it validates data types for high integrity.
Manual Source Management
Having to log into a separate portal just to pause or update credentials on a source connection.
Keep control in chat. Use update_source or update_destination to manage and modify connections without leaving your current workflow.
When It Fits, When It Doesn't
Use this server if you need to programmatically control the movement of structured data between distinct systems (ETL/ELT). You must be able to define sources, specify destinations, and trigger syncs manually. Don't use it if your primary need is complex, multi-stage transformation logic within the loading process—this server handles connection and transfer. If you only need real-time data streaming (like reading a live Kafka topic), this isn't the right tool; you need a dedicated stream processor. However, if you just need to read raw files from S3 or check basic sync status without credentials, simple file management tools suffice. This server is for managing pipelines and data integrity across known systems.
Questions you might have
How do I check my data pipeline health using Stitch Data? +
You run the get_import_status tool. This confirms if the core Import API is running correctly. You can also use list_extractions to see detailed logs about recent job runs.
Can I load custom records using Stitch Data? +
Yes, you use the push_import_batch tool. This is best because it validates your data types before loading into your warehouse, keeping your data clean.
I need to connect a new source; which tool do I use? Stitch Data? +
Start by using list_source_types to confirm compatibility. Then, run the create_source tool to register the connection with the system.
What if my destination needs updating? Use Stitch Data. +
Use the update_destination tool. This lets you change credentials or settings for an existing warehouse without having to delete and re-create the entire connection.
How do I generate temporary credentials for frontend integration using Stitch Data? +
Use the create_ephemeral_session tool. This generates a time-limited token specifically for your Connect JavaScript client, keeping your core credentials safe while letting you build out front-end integrations.
Using Stitch Data, how do I control which specific tables or fields get replicated? +
You manage this via the update_stream_metadata tool. After listing available streams with list_streams, you select the exact fields and data types for replication, preventing unnecessary data transfer.
Before I push a large batch of records, how can I validate my credentials and the data format using Stitch Data? +
Run validate_import_data. This checks your formatting and authentication without actually writing any data to your warehouse. It's an essential pre-flight step for reliable ingestion.
If I need to force a sync or check past replication job histories with Stitch Data, what tools do I use? +
Start the process by using start_sync to manually trigger a replication job. Then, check historical runs by calling list_extractions or list_loads to monitor successful and failed attempts.
How can I verify if my data format is correct before pushing it to the warehouse? +
You can use the validate_import_data tool. It functions exactly like a data push but only tests your credentials and data formatting without actually persisting any records.
Is there a way to check if the Stitch Import API is currently operational? +
Yes, use the get_import_status tool. It checks the operational status of the Import API and returns whether it is functioning correctly.
How do I see which types of sources I can connect to my Stitch account? +
Run the list_source_types tool. It will provide a comprehensive list of all available source connectors supported by Stitch.
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