Fauna MCP for AI. Query Your Database Directly from Conversation
Works with every AI agent you already use
…and any MCP-compatible client








How this MCP server connects to your AI agent
Fauna (Serverless DB) connects your AI agent directly to a Fauna serverless database. Use this MCP to run complex FQL queries, letting you read, write, or modify data collections and indexes entirely through natural conversation.
Forget switching tabs; manage your entire data infrastructure right where you're already working.
What AI agents can do with Fauna (Serverless DB) Automation
Execute fql
Runs any Fauna Query Language (FQL) command to read, create, update, or delete data in the database.
Run FQL commands to create new records, update existing ones, or delete specific entries in the database.
Query indexes and collections to fetch specific user profiles or product details by criteria you define.
List available collections and inspect the current schema of your database using standard FQL commands.
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What AI agents can do with Fauna (Serverless DB) with 1 tool
Use the available tools to run full Fauna Query Language commands, allowing you to manage collections, documents, and indexes directly through conversation.
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 Fauna (Serverless DB) on VinkiusExecute Fql
Runs any Fauna Query Language (FQL) command to read, create, update, or delete data in the database.
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 Fauna (Serverless DB), 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 Fauna. 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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Policy on every call
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~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 1 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Dealing with Database Context Switching, Solved with Vinkius AI Gateway
Today, if you need to check a piece of data—say, confirming if an order was processed or listing available user roles—you have to stop what you're doing. You switch from your coding environment to the Fauna dashboard. You navigate through collections and indexes, manually typing out filters until you find the right document. Then you copy the result back into your chat for context.
With this MCP, you keep everything in one place. Your agent acts as if it's already logged into the database console. You simply tell it what data to look up or what record needs updating, and it executes the complex `execute_fql` query behind the scenes. You get the precise result without ever leaving your chat interface.
Execute FQL Queries with Fauna (Serverless DB)
The manual steps that vanish are navigating multiple interfaces and writing boilerplate connection code. Before, you had to write the setup function; now, you just talk about what needs to be done. You don't worry about whether the credentials are correct or if the proper query language syntax is used.
It’s a massive leap in efficiency. Your agent handles the technical plumbing—the FQL execution—letting you focus entirely on the business logic and the data itself. It just works.
What your AI can actually do with this
Connecting your AI client to Fauna changes how developers interact with their databases. Instead of opening the dashboard and writing complex API calls manually, you talk to your agent and it handles the backend work using powerful FQL queries. You can tell your agent exactly what data you need—whether it's fetching a specific user profile or listing every collection in the database.
It’s like having an expert developer sitting next to you who remembers all your table names and query structures. This ability means you never lose context, keeping your focus on the logic of your application, not the plumbing of your data layer. When you subscribe through Vinkius, you gain instant access to this powerful database management tool alongside thousands of others, making it a central piece of your development stack.
You can pass complex JSON structures safely into queries and even inspect your schema without writing boilerplate code.
019e3894-41f2-726f-8b63-6c73994c4093 Here's how it actually works
The bottom line is you stop using the Fauna dashboard for quick checks; your AI client does the heavy lifting for you.
Subscribe to this MCP on Vinkius and provide your Fauna secret credentials.
Select this connection in your AI client, allowing it to authenticate against your database.
Ask your agent a question like 'List all records in the Orders collection' or 'Update user 123’s status,' and it executes the necessary FQL query.
Who is this actually for?
Backend developers and data engineers who are tired of context switching between their code editor, a chat window, and a database management console. This MCP lets them treat the database like just another API endpoint.
Testing FQL queries or verifying data state directly within the agent's conversation flow before committing code.
Performing quick, one-off data cleanups, migrations, or schema checks without needing to write a full script or interact with a CLI tool.
Retrieving real-time usage metrics or user segment data directly from the database by prompting their agent, bypassing custom admin panels.
What Changes When You Connect
Eliminate context switching. You manage your collections and indices directly within your AI agent, eliminating the need to jump between chat windows and the Fauna dashboard.
Run full CRUD operations using natural language prompts. The execute_fql tool lets you read, write, update, or delete data without manually structuring complex API calls every time.
Quickly validate logic for developers. Test FQL queries on live data to verify state changes immediately, making debugging much faster than running local scripts.
Handle complex data inputs safely. You can pass structured JSON arguments into your queries using the tool's parameters field, ensuring clean and predictable execution.
Inspect the whole system easily. Use the MCP to list collections or query indexes, giving you a real-time understanding of your database structure right from the chat.
See it in action
A user needs to find all products marked 'out of stock.'
Instead of building a complex search query in a separate tool, the user asks their agent: 'Find all documents in Products where inventory is zero.' The agent uses execute_fql to run the precise query and returns a list of IDs. Problem solved.
A data engineer needs to log an unusual system event.
The engineer simply prompts: 'Create a new Log entry for 'System maintenance started' with timestamp.' The agent executes execute_fql and logs the record instantly, without touching any CLI or web form.
A product team wants to verify user sign-up data.
The PM asks: 'Show me all user records created yesterday with a premium status.' The agent runs execute_fql, pulling the exact, filtered dataset needed for their report.
A developer needs to test an update query before deployment.
The developer prompts: 'Pretend I'm updating user 456’s email. Run a dry-run check.' The agent runs execute_fql and returns the expected data structure without actually changing anything.
The honest tradeoffs
Using manual API calls for simple reads
Manually constructing a GET request endpoint in code just to list all available collections or check if an index exists.
Ask your agent to use the MCP's capability to inspect schema metadata. Prompt: 'List all available collections.' This is faster and requires zero boilerplate code.
Copy-pasting large data dumps for debugging
Having to export a massive table of raw database results into CSV format, then pasting it back into the chat window for review.
Ask your agent to run a filtered execute_fql query with specific parameters. It retrieves only the necessary data points, keeping the conversation focused.
Assuming schema structure
Writing code that assumes a collection name or field name exists when it was recently refactored by another team member.
First, prompt your agent to 'List all collections.' Then, use the tool's documentation and the results of that call to build your query. Always confirm the schema first.
When It Fits, When It Doesn't
Use this MCP if your core workflow involves frequently checking or modifying structured data within a serverless NoSQL environment (like Fauna). You need an agent capable of translating natural language into precise, executable FQL commands to perform CRUD operations. Don't use it if you are only handling unstructured text documents; for those, a general file storage connector is better. If your primary task is building complex application logic that requires state management across multiple services (e.g., payment processing + inventory), you need an orchestration tool like LangChain or CrewAI to coordinate the steps. This MCP is purely focused on being the developer's window into the database itself.
Questions you might have
How does Fauna (Serverless DB) MCP work with my existing code? +
The MCP acts as a wrapper. Instead of your code having to make direct, complex API calls to the database, you tell your agent what action is needed, and it uses execute_fql to run the query for you.
Can I only use this MCP for reading data? +
No. The execute_fql tool supports full CRUD capabilities. You can read documents, but you can also create new records or update existing ones with simple instructions.
What kind of language does the execute_fql tool use? +
The tool executes Fauna Query Language (FQL). This is the standard query language for Fauna's serverless database, designed to handle complex data relationships efficiently.
Is this MCP only for developers? +
Not necessarily. While built for developers, anyone who needs reliable access to structured business data—like product managers or analysts—can use it via natural language prompts.
Does using Fauna (Serverless DB) MCP require changing my database structure? +
No. The MCP allows you to inspect and manage the schema, but the queries themselves are designed to read and write based on your existing definitions.
We've already built the connector for Fauna. Just plug in your AI agents and start using Vinkius.
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