Hasura MCP. Run Queries, Analyze Plans, and Manage Schema from Chat.
Gemini Works with every AI agent you already use
…and any MCP-compatible client
Just plug in your AI agents and start using Vinkius.
Hasura (Instant GraphQL & REST Engine) connects your AI agent directly to your database layer. Execute complex queries, manage metadata, analyze SQL execution plans, and monitor server health—all through natural language.
It acts as a single pane of glass for database operations without you writing boilerplate code.
What your AI agents can do
Check health
Checks the Hasura server's operational health status, optionally validating metadata consistency.
Execute graphql
Runs any complex GraphQL query or mutation against your database endpoint.
Execute metadata
Performs specific operations on the Hasura Metadata API to manage schema definitions.
Execute complex queries or mutations against your database using the execute_graphql tool.
Programmatically track tables, check relationships, and manage permissions with the execute_metadata tool.
Inspect the generated SQL and execution plan for any GraphQL query using explain_graphql to identify bottlenecks.
Verify overall server health and consistency of metadata by calling check_health.
Get the current Hasura server configuration details using get_config.
Run a full database dump (pg_dump) on connected Postgres sources for backup or migration tasks.
Ask AI about this MCP
Supported MCP Clients
Gemini Waiting for input…
Hasura (Instant GraphQL & REST Engine) MCP Server: 7 Tools for Data Ops
Use these tools to execute complex queries, manage schema metadata, analyze SQL plans, and monitor the health of your Hasura data layer.
019e5d23check health
Checks the Hasura server's operational health status, optionally validating metadata consistency.
019e5d23execute graphql
Runs any complex GraphQL query or mutation against your database endpoint.
019e5d23execute metadata
Performs specific operations on the Hasura Metadata API to manage schema definitions.
019e5d23explain graphql
Analyzes a GraphQL query, translating it into SQL and showing the database's execution plan for performance analysis.
019e5d23get config
Retrieves the current configuration settings for the Hasura server instance.
019e5d23get version
Gets basic information about the running Hasura server version and type.
019e5d23pg dump
Executes a full database dump operation on your connected Postgres source for quick backups.
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 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
This Hasura engine connects your AI agent straight into your database layer. You can execute complex queries, manage metadata definitions, analyze SQL execution plans, and check server health—all through natural language commands. It’s like having a single pane of glass for all your database operations without you gotta write boilerplate code.
Running Complex Data Requests
To get data from your source, you use execute_graphql. This tool lets your agent run any complex GraphQL query or mutation against the endpoint. You can handle variables and batch multiple requests in a single call. If you need to change something—say, creating a new record or updating user profiles—that’s a mutation job for this tool.
Managing Your Data Schema and Structure
You don't wanna mess up your schema while coding, so execute_metadata lets your agent programmatically manage everything. You can track tables, check relationships between them, and even adjust permissions right through the Metadata API. This means you control who sees what and how the data is organized without opening a separate admin panel.
Deep Dive Performance Analysis
When a query runs slow, you gotta know why. explain_graphql takes any GraphQL query you throw at it and analyzes it by translating that request into SQL. It then shows you the database's actual execution plan. This lets your agent identify bottlenecks—you can see exactly where the slowdown is happening so you can fix it before users even notice.
System Health, Configuration, and Versioning
Before running big queries, you gotta make sure everything’s solid. You call check_health to verify the server's overall operational health status; this also validates that your metadata is consistent. Need to know what version of Hasura you're running? Use get_version. For full setup details—like what settings are active right now—you run get_config to grab the current configuration settings for the whole server instance.
Database Backups and Migration
For backups or migrating data, pg_dump executes a full database dump operation on your connected Postgres source. You'll get a complete export of the entire database schema, which is killer for quick rollbacks or moving to a new environment.
How Hasura MCP Works
- 1 Subscribe to the server and provide your Hasura GraphQL Endpoint URL.
- 2 Enter your Admin Secret key for secure access. This keeps all operations authenticated.
- 3 Tell your AI client what you need—e.g., 'Check if the users table has an index on email.' The agent then runs the appropriate tool call.
The bottom line is, it lets your agent manage and analyze your data layer using simple conversational commands.
Who Is Hasura MCP For?
Backend developers who hate context switching. Data engineers stuck in manual ETL processes. DevOps ops staff constantly checking dashboards for service degradation. This server gives you direct, programmatic control over the database without leaving your chat or IDE.
Writing API endpoints; they use this to quickly test mutations and verify schema changes using execute_graphql before committing code.
Running data migrations or performing quick backups. They rely on pg_dump and explain_graphql to ensure the new schema works fast.
Monitoring production services. They check system stability by running check_health and verifying configurations with get_config.
What Changes When You Connect
- Stop context switching. Instead of opening a separate SQL tool, your agent runs complex data queries directly using
execute_graphqlwithin the chat window. You see results instantly. - Find slow spots before they break production. The
explain_graphqltool analyzes the generated SQL and shows you exactly where query performance drops off—no guessing required. - Maintain compliance effortlessly. Use
check_healthto quickly verify metadata consistency across environments, ensuring your schema hasn't drifted since deployment. - Simplify migrations. When you need a backup or want to test a new schema version, the
pg_dumptool handles the export in one call. - Get immediate status checks. Need to know if Hasura is running right? A simple call to
check_healthgives you real-time confirmation without logging into a dashboard.
Real-World Use Cases
Debugging a Slow API Endpoint
The agent detects that the 'User Profile' endpoint is slow. It runs explain_graphql on the query, which reveals an Index Scan issue with cost 12.40. The engineer knows exactly what part of the schema needs optimization.
Schema Drift Check
The DevOps team suspects a recent deployment corrupted metadata. They run check_health in strict mode. The tool immediately reports inconsistency, letting them roll back or fix permissions via execute_metadata before users notice.
Preparing for an Audit
A data team needs a full backup of the user table structure. They ask their agent to run pg_dump. The tool executes the dump on the Postgres source, providing a clean, exportable schema snapshot.
Initial Setup Verification
A new developer needs to confirm that all connected services are talking to the correct Hasura version. They use get_version and then run get_config to validate the full operational setup parameters.
The Tradeoffs
Writing Raw SQL in the Chat
Trying to paste complex, multi-line raw SQL queries into the chat box because you forgot the proper tool. This is often insecure and fails validation.
→
Use execute_graphql. Instead of writing SQL, describe your data need (e.g., 'fetch user IDs and names from last week'). The agent translates that into a query and runs it for you.
Ignoring Performance Metrics
The API works but feels slow. The developer just assumes the database is fine, wasting time on general debugging.
→
Run explain_graphql. This tool forces a performance deep-dive, showing you the actual Index Scans and costs. It tells you why it's slow.
Assuming Schema Consistency
Code breaks in production because an unrelated deployment changed a table name or relationship setting, but no one checked first.
→
Run check_health with strict mode. This verifies metadata consistency before your agent attempts any data operations.
When It Fits, When It Doesn't
Use this server if your primary need is operational visibility and programmatic control over the Hasura layer. You're dealing with complex, relational data, and you need to run structured queries (GraphQL) or manage schema definitions (Metadata API).
Don't use it if: 1) Your task is purely front-end rendering; this handles the backend connection only. 2) You need real-time streaming data that exceeds what pg_dump can capture, in which case you might need a specialized event bus tool. 3) You are building simple CRUD operations for a single entity and just need basic API calls—a generic REST client might suffice. But if your task involves analyzing the schema or debugging performance, this is what you need.
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
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 7 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Database interactions shouldn't feel like navigating three different interfaces.
Today, running a simple query requires context switching. You write code in your IDE, then copy the schema definition to an introspection tool, run a sample query in a separate client, and finally check performance metrics on another dashboard. It's slow, clunky, and you lose track of where things broke.
With this MCP server, that process collapses into one conversation. You tell your agent, 'Give me the list of all active users who signed up last month.' The tool runs `execute_graphql`, shows you the data, and if it's slow, you can immediately run `explain_graphql` for the fix—all without leaving your chat.
Hasura (Instant GraphQL & REST Engine): Query & Metadata Tools
The biggest time sink is usually schema verification. You spend hours manually verifying if a new table relationship was added correctly or if permissions were updated across multiple environments.
Now, you simply use `check_health` and `execute_metadata`. The tool verifies the entire metadata layer automatically, confirming that your schema definitions are consistent everywhere. It's an instant operational audit.
Common Questions About Hasura MCP
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.
When running complex actions with `execute_graphql`, how do I ensure that multiple mutations happen as a single, atomic transaction? +
You must wrap your changes in a dedicated batch or transactional mutation within the query. The tool executes these requests against the underlying database engine, guaranteeing atomicity; either all updates succeed, or none of them take effect.
If I run into performance issues, what specific details does `explain_graphql` provide to help me optimize my queries? +
explain_graphql generates the underlying SQL and shows the database's execution plan. It pinpoints which parts of the query consume the most resources (like slow index scans), letting you target your optimization efforts precisely.
I need to verify my server setup; what critical information can I pull using the `get_config` tool? +
The get_config tool retrieves the live operational configuration parameters for your Hasura instance. This lets you check things like connection limits, enabled features, or specific runtime settings without logging into the dashboard.
If I need to adjust table permissions or relationships programmatically, what does `execute_metadata` allow me to do? +
execute_metadata gives you direct access to the Metadata API. You can manage schema definitions, modify row-level security policies, and update data relationships without needing manual UI interaction.
When connecting my AI agent to Hasura via `execute_graphql`, what is the best way to handle rate limits or excessive usage? +
The system monitors server health and provides error codes if you hit resource limitations. You'll receive specific HTTP status messages or database errors that indicate when a rate limit has been reached, allowing your agent to back off.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
More in this category
LicenseCheck API
Search software licenses — audit permissions and conditions via AI.
AgentOps (Agent Telemetry and Monitoring)
Monitor and observe your AI agents with AgentOps — track traces, spans, and project metrics directly from your agent.
Password Manager Export Analyzer
Analyze Bitwarden, LastPass, or 1Password CSV exports for weak and duplicate passwords — without EVER sending real passwords to the AI.
You might also like
SEC EDGAR Filings — 10-K, 10-Q, 8-K, Insider Trades & Full-Text Search
Access all SEC filings: 10-K annual reports, 10-Q quarterly reports, 8-K material event disclosures, Form 4 insider trading data, and full-text search across every document ever filed on EDGAR. Filter by company, form type, and date range.
Care Quality Commission (CQC)
Access UK health and social care regulator data via CQC — search providers, check ratings, and retrieve inspection reports directly from any AI agent.
CoinGecko
Enable your AI agent to query crypto prices, market rankings, and global market stats via the CoinGecko API.