ElectricSQL MCP. Stream Live Postgres Data into Your AI Agent's Context
ElectricSQL Sync Engine MCP brings real-time data from Postgres directly to your AI client. It lets you fetch structured subsets of database information, track changes over time, and query live application data right within your chat window.
Give Claude and any AI agent real-world access
Fetch a complete, historical snapshot of a data table using an initial sync call.
Query specific groups of records—for example, only 'pending' orders in the last month—using advanced filtering methods.
Keep your agent continuously updated as data changes in the underlying Postgres database via long-polling or SSE.
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What AI agents can do with ElectricSQL (Sync Engine) with 2 Tools
These tools let you programmatically retrieve data from Postgres by fetching initial snapshots or submitting highly detailed queries for specific subsets of records.
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 ElectricSQL (Sync Engine) MCPGet Shape
Use this tool to pull an initial snapshot of data from Postgres using a standard GET request.
Post Shape
Send complex queries and sync subsets of data out of Postgres by submitting them via...
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.
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 each call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with ElectricSQL (Sync Engine), then connect any of our 5,200+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,200+ others, all in one place
- Add new capabilities to your AI anytime you want
- Connections are secured and governed automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog weekly
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by ElectricSQL. 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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No stored credentials
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Database state used to be hard to access for AI
Today, if your AI needs to know something—like the current inventory count or a user’s latest activity—you usually have to write dedicated API endpoints. That means building an endpoint just for 'user status,' another one for 'order history,' and then linking them all up so they don't time out when data changes.
With this MCP, you bypass that whole process. You connect your agent once, point it at the Postgres database, and the system handles the complexity of fetching, filtering, and streaming live data shapes directly into your chat context. Your agent just knows how to ask.
ElectricSQL Sync Engine MCP gives you real-time data insight
The manual steps that disappear are writing the complex backend plumbing and managing state across multiple services. You eliminate the need for dedicated 'status check' endpoints, simplifying your entire architecture.
Now, your AI agent can treat the database as a living source of truth. It doesn't just answer based on what it was trained on; it answers based on exactly what's happening right now.
What ElectricSQL MCP does for your AI
This MCP connects your AI agent straight into a Postgres database, letting it stream live data without needing complex backend setups. You can ask questions about specific tables or monitor system state in real-time. It handles initial data dumps—getting the whole picture at once—and then keeps you updated as records change, using efficient log offsets for incremental syncs.
Need to query a niche group of records? Use advanced filtering and subset snapshots, letting your agent handle complex 'WHERE' clauses that would normally break a URL. Because Vinkius hosts this MCP, connecting it is simple: link up once from any compatible client, and you immediately gain access to live database context for analysis or debugging.
019e388f-b8d8-70dd-990e-603c981098be How to set up ElectricSQL MCP
The bottom line is that your AI client sees your Postgres data as if it were local memory, giving you live application insight without writing a single integration script.
Subscribe to this MCP and provide your specific Electric Service URL, pointing it toward your live Postgres instance.
Your AI client initiates a data request using the appropriate method—either requesting an initial sync or setting up a continuous stream.
The agent receives structured, filtered data (a 'shape') directly into its context, allowing it to answer questions based on the current database state.
Who uses ElectricSQL MCP
This MCP is for developers and engineers who can't afford to waste time building fragile, custom APIs just to give their AI agent data access. It’s perfect for anyone needing live system state—from debugging production issues to letting product teams monitor real-time user activity.
Building internal tools where the AI needs immediate, verifiable access to application records (e.g., 'What are all the users who signed up in the last hour?').
Testing data pipelines or debugging complex ETL processes by letting the agent inspect specific database 'shapes' and sync subsets.
Monitoring real-time user activity or system health directly within a chat interface without needing to open up multiple dashboards.
Benefits of connecting ElectricSQL MCP
You get live data streams. Instead of polling a database every few seconds, the agent stays connected and gets instant updates whenever records change.
Complex filtering is simple. You don't have to write complicated API endpoints for niche queries; you just tell the agent what subset of data you need.
It handles massive datasets efficiently. The built-in support for limits and offsets means you can browse huge tables without hitting memory or URL length restrictions.
Use get_shape for initial dumps. When you start a project, you can pull an entire table snapshot quickly using this tool to establish context immediately.
The whole process avoids complex plumbing. You connect your AI agent through Vinkius, and the data sync handles itself, letting you focus on logic, not infrastructure.
ElectricSQL MCP use cases
Debugging a User Flow Issue
A backend developer notices a user reported an incorrect order total. Instead of running manual SQL queries across multiple tables, they ask their agent to use post_shape to query the 'orders' and 'line_items' tables for that specific user ID. The agent returns all relevant data shapes, confirming where the calculation failed.
Monitoring System Health
A product manager needs to see if a new feature is causing errors in real-time. They set up a live sync on the 'error_logs' table. As soon as an error is written to Postgres, the agent immediately alerts them and provides the full context of the failure.
Analyzing Campaign Leads
A marketing team wants to see all leads from a specific zip code who haven't opened an email in 30 days. They use post_shape, defining complex WHERE clauses for both geography and activity status. The agent returns only the actionable subset of contacts.
Initial Project Context Build
A new data analyst needs to understand a legacy system's structure. Using get_shape with an offset=-1, they pull a full snapshot of the 'user_profiles' table, giving their agent enough context to answer structural questions immediately.
ElectricSQL MCP tradeoffs
What to watch out for, and the recommended way to handle each one.
Manual API Calls for State Checks
Manually writing and maintaining dozens of endpoint calls (e.g., /api/user-status, /api/inventory?id=123) to check the current state of a system.
Connect this MCP instead. Use the agent's built-in data streaming capabilities or post_shape to query the underlying Postgres tables directly. The agent handles the API structure for you.
Relying on Cached Data
Building an AI workflow that relies on a data cache refreshed every hour, meaning critical information is always outdated when the user asks about it.
Use this MCP's real-time sync capabilities. It keeps your agent long-polling against Postgres, ensuring any change in the database is instantly visible to your query.
Ignoring Data Structure
Trying to pass an entire, unfiltered Postgres table into the chat context because it 'might contain what you need.' This overloads the agent and introduces noise.
Use post_shape. Define specific subsets of data using precise filtering (WHERE clauses) and column selection. This keeps the information clean and actionable for your agent.
When to use ElectricSQL MCP
Use this MCP if your core problem is getting live, structured application state—data that lives in a database—into an AI workflow without building complex middleware. You need to monitor changes or run specific queries against tables like 'orders' or 'user_logs.'
Don't use it if you only need to process unstructured files (like PDFs) or interact with simple APIs that don't involve relational data. For those cases, a file ingestion MCP is better.
If your goal is simply to run a single SQL query and get one result without needing real-time context, a standalone database querying tool might suffice. But if you need the agent to know what changed over time, this MCP's streaming capabilities are necessary.
Frequently asked questions about ElectricSQL MCP
How does ElectricSQL Sync Engine MCP handle large datasets? +
It supports pagination and limits, allowing you to efficiently browse massive tables without overloading the system. This prevents errors related to dataset size.
Is this good for continuous monitoring? +
Yes. You can enable live streaming via long-polling or SSE, meaning your agent updates instantly whenever new rows are added to a table like 'messages'.
Do I need to write SQL code to use the MCP? +
No. While the underlying mechanism uses SQL concepts (shapes and WHERE clauses), you interact with it naturally by simply asking your agent questions about the data.
What is the difference between get_shape and post_shape? +
Use get_shape for initial, basic snapshots. Use post_shape when you need to send complex filtering parameters or detailed subsets of data that exceed simple GET request limits.
Can I sync multiple tables at once using ElectricSQL Sync Engine MCP? +
Yes, you can define and manage shapes across several different Postgres tables within the same agent workflow to get a holistic view of your application state.