Hasura MCP for AI. Query, Inspect, and Manage Your API Layer
Works with every AI agent you already use
…and any MCP-compatible client








How this MCP server connects to your AI agent
Hasura connects your GraphQL engine directly to your AI agent, letting you manage complex database operations through natural language. Use this MCP to run queries, check metadata consistency, analyze SQL performance plans, and trigger database dumps without leaving your chat interface.
What AI agents can do with Hasura (Instant GraphQL & REST Engine) Automation
Check health
Monitors the overall status of your Hasura system, optionally verifying metadata consistency.
Execute graphql
Runs any complex GraphQL query or mutation against your data source.
Execute metadata
Allows you to perform operations directly on the Hasura metadata API for schema management.
Execute complex data requests and writes against your database's primary endpoint.
Manage table definitions, relationships, and permissions programmatically using the dedicated API.
Generate SQL execution plans for any GraphQL query so you can pinpoint bottlenecks in your database code.
Verify the overall status of the Hasura instance, including checking if metadata is consistent.
Initiate a full schema dump for connected Postgres sources when you need to move or archive data.
Ask an AI about this
Waiting for input…
What AI agents can do with Hasura (Instant GraphQL & REST Engine) - 7 Tools
This set of tools lets you interact with every aspect of your Hasura data layer: running queries, managing schema details, and analyzing performance plans.
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 Hasura (Instant GraphQL & REST Engine) on VinkiusCheck Health
Monitors the overall status of your Hasura system, optionally verifying metadata consistency.
Execute Graphql
Runs any complex GraphQL query or mutation against your data source.
Execute Metadata
Allows you to perform operations directly on the Hasura metadata API for schema...
Explain Graphql
Analyzes a GraphQL query and returns the underlying SQL execution plan, highlighting...
Get Config
Retrieves the current operational configuration details for your Hasura instance.
Get Version
Fetches specific version and type information about the running Hasura service.
Pg Dump
Executes a full database dump of a connected Postgres source for schema archiving or transfer.
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 every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with Hasura (Instant GraphQL & REST Engine), 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 Hasura. 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
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 7 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Debugging API calls used to be a mess of switching tabs and running manual commands.
Today, if your query is slow or fails, you have to juggle multiple places: the GraphQL playground for testing, the terminal for schema dumps, and separate documentation pages just to check configuration variables. You copy-paste a query here, run it there, then jump to another system just to see what SQL was actually generated underneath.
With this MCP connection, all those steps collapse into one conversation with your agent. You ask the question, and the tool handles the execution, analysis, and reporting of results—giving you immediate visibility into everything from the query result to the underlying performance metrics.
Query & Manage Database Data
You used to have to manually call `execute_graphql` for data, then run a separate command just to check if that query was efficient. You'd need another tool to dump the schema when moving environments.
Now, your agent can chain these actions. It runs `explain_graphql` on the problematic code you provided, tells you where it needs an index, and then lets you use `pg_dump` to quickly get a clean backup of that table for testing.
What your AI can actually do with this
This connection lets you talk to your backend database like it's a simple API endpoint. You can execute full GraphQL mutations or read complex data structures just by describing what you need. Need to know if the schema is consistent? Call check_health. Want to optimize a slow query? Use explain_graphql to see exactly where Postgres is struggling.
It’s also useful for maintenance; trigger a quick dump of your database tables using pg_dump, or manage permissions by executing metadata operations. Because Vinkius hosts this MCP, you get access to all these critical developer functions from one single connection point, no matter what AI client you prefer.
019e5d23-26d2-7212-bc1f-305fc85bfd05 Here's how it actually works
The bottom line is, you tell your AI client what data you need, and it sends the structured request to Hasura, which runs the query against your Postgres database.
Subscribe to this MCP and provide your Hasura GraphQL Endpoint URL.
Supply the required Admin Secret key for secure connection authorization.
Your agent can then call tools like execute_graphql or explain_graphql using natural language prompts.
Who is this actually for?
Backend developers who spend too much time jumping between their IDE terminal and a separate UI just to validate a single data query. It's for anyone whose day involves touching the core API layer.
Runs queries, validates mutations, and uses execute_graphql to test endpoint functionality without restarting services.
Inspects slow queries using explain_graphql, checks data consistency with check_health, or pulls schema dumps via pg_dump for migration testing.
Monitors the health and configuration of the Hasura layer using get_config and verifies version details with get_version across multiple environments.
What Changes When You Connect
Stop guessing if your query will run. explain_graphql gives you the actual SQL execution plan so you can optimize performance before deployment.
Avoid manual schema checks. Run check_health to instantly verify metadata consistency across all connected services, preventing runtime errors.
Handle everything from data reads to writes in one place. Use execute_graphql for complex mutations or simple queries without leaving your agent chat.
When you need a copy of the schema, don't write a script. Simply call pg_dump to export full database backups instantly.
Manage system details cleanly. Get runtime info using get_config and verify the exact version with get_version, making environment parity easy.
See it in action
A new feature breaks in production
The Ops Engineer notices a query is timing out. Instead of logging into the database UI, they ask their agent to run explain_graphql on the problematic query. The results immediately show that an index scan is failing, allowing them to pinpoint the exact missing index and fix it.
Migrating data between environments
The Data Engineer needs a fresh copy of the current user table schema for staging. They prompt their agent to use pg_dump. The tool runs the export, providing a clean SQL dump file ready for import into the testing environment.
Validating new API endpoints
The Backend Developer builds a mutation and wants to test it against live data. They use execute_graphql with their agent client, running the full query in natural language. The response confirms successful execution, validating the endpoint before code review.
Troubleshooting inconsistent permissions
The DevOps team suspects a configuration drift between staging and production. They ask the agent to run get_config and compare the output against expected values, quickly identifying which specific setting is different.
The honest tradeoffs
Assuming all data retrieval is safe
Running a complex query using only execute_graphql when you suspect performance issues. You get the answer, but you don't know why it was slow, forcing manual SQL investigation.
Before running any resource-heavy query, run explain_graphql. This tool analyzes the query and shows the underlying execution plan, telling you exactly where Postgres is spending time.
Mixing up metadata management
Trying to change a table relationship or permission by just calling execute_graphql with a mutation. The API might reject it because that requires specific administrative calls.
Use the dedicated execute_metadata tool for schema changes, managing relationships, and updating permissions programmatically.
Over-relying on chat logs for system state
Relying on memory or documentation to remember if the Hasura instance is actually healthy or what its current version is. This leads to guesswork when deployment fails.
Always start by calling check_health and then run get_version. These tools provide definitive, real-time status checks.
When It Fits, When It Doesn't
Use this MCP if your work revolves around the core API layer: executing queries, managing schema details, or checking backend health. If you need to perform a simple CRUD operation and don't care about performance or metadata, maybe a simpler tool works. However, if you are doing any serious development—especially optimizing query speed, migrating schemas (pg_dump), or confirming consistency (check_health)—you absolutely need the full set of tools here. Don't use this if your only goal is generating content; stick to specialized LLMs for that. This is purely a developer utility suite.
Questions you might have
Can I see the actual SQL query that Hasura generates for my GraphQL request? +
Yes! Use the explain_graphql tool. It will return the generated SQL and the execution plan from the database, helping you debug performance issues.
How do I check if my metadata is inconsistent or if the server is down? +
You can use the check_health tool. If you set the strict parameter to true, it will specifically check for metadata consistency in addition to basic connectivity.
Is it possible to export my database schema through this server? +
Yes, the pg_dump tool allows you to execute a schema dump on your connected Postgres source, which is useful for migrations or backups.
How do I use `get_config` to see my Hasura server settings? +
It retrieves the current operational configuration of your Hasura instance. This allows you or your agent to confirm global settings, like API key requirements or default connection parameters.
What kind of operations can I run using `execute_metadata`? +
You can perform programmatic actions on the schema itself. This includes tracking tables, defining new relationships between models, and managing user permissions via the Metadata API.
Can I use `execute_graphql` to make changes or mutations to my data? +
Yes, execute_graphql supports not only queries but also write operations (mutations) and batched requests. You can pass variables directly to perform complex updates on your database.
How do I check the specific version details of my Hasura engine using `get_version`? +
Calling get_version returns the exact software type and running version number. This is useful for auditing or ensuring compatibility when deploying new services.
Does this MCP handle advanced data manipulation beyond simple reading, like batching requests via `execute_graphql`? +
Yes, it handles batched requests and supports full variable support within GraphQL operations. You can pass multiple related queries or mutations in a single call.
We've already built the connector for Hasura. Just plug in your AI agents and start using Vinkius.
No hosting. No infrastructure. No complex setup.
All 7 tools are live and waiting.
You're up and running in seconds.
Vinkius gives your AI agents access to the full catalog of app connectors, all fully managed, secure, and enterprise-ready. One subscription, every tool you need.
Built, hosted, and secured by Vinkius. You just connect and go.