How to Use the Fauna (Serverless DB) MCP in AutoGen

Let your AutoGen agents debate and coordinate database updates directly via serverless FQL queries.

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Connect Fauna (Serverless DB) MCP to AutoGen

Create your Vinkius account to connect Fauna (Serverless DB) to AutoGen and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Fauna (Serverless DB) with AutoGen

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Execute FQL queries inside AutoGen conversations

The `execute_fql` tool on this MCP Server enables your AutoGen agents to interact with your serverless database during their collaborative discussions. One agent can draft a query to modify a schema, while another agent inspects the statement before running it. This multi-agent verification ensures database operations are safe and accurate. McpToolAdapter handles all schema conversions automatically behind the scenes. This allows your agents to pass structured database results back and forth as plain text or JSON inside their chat logs.

Coordinate database updates using this MCP Server

By executing `execute_fql`, this database server allows you to build consensus-driven workflows for managing your serverless data. For example, a performance agent might analyze a slow query, while a security agent checks it for injection risks before executing it. Only when both agents agree does the system call the tool. This setup reduces the risk of accidental data corruption or unauthorized schema changes. You get a built-in audit trail of the discussion leading up to every database modification.

Debate schema designs and execute updates autonomously

Your agents can use this MCP Server to inspect your current database schema and suggest improvements with the `execute_fql` tool. They discuss index optimizations or collection structures directly in the chat. Once they reach a consensus, the executing agent runs the migration query. You don't have to manually review and run every single schema change. The agents handle the analysis, write the FQL, and execute the changes while keeping you in the loop.

Setup guide

Set up Fauna (Serverless DB) MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Fauna (Serverless DB) tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Fauna (Serverless DB)_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Fauna (Serverless DB) data")
print(result.messages[-1].content)

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Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Fauna (Serverless DB)_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Fauna (Serverless DB) data")
    print(result.messages[-1].content)

Get your connection token →

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Common questions about Fauna (Serverless DB) MCP in AutoGen

Use the MCP server tools adapter with your HTTP server parameters. Pass the resulting tool list to your assistant agent's constructor to give it database access.

Yes, any agent in your conversation group that has been granted the tool list can invoke the `execute_fql` tool. The agents can share query results and debate the outcomes in their chat thread.

If a query fails, the tool returns the error message directly to the calling agent. The agent can then discuss the error with other agents in the group to troubleshoot and rewrite the FQL query.

This MCP Server supports both stdio and streamable HTTP transports. You can configure the connection parameters to match your deployment environment and agent architecture.

Your data payloads and FQL queries are processed inside a zero-trust, ephemeral V8 isolate sandbox. No query history or database records are stored permanently on the hosting platform, keeping your operational data confidential.

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