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

Q: Can LlamaIndex index the raw outputs of Fauna (Serverless DB) queries?

Yes, the output from the executefql tool is returned as structured JSON which LlamaIndex can parse into document nodes. These nodes are then ingested and indexed into your vector store for semantic search.

Index live Fauna serverless database records directly into your LlamaIndex vector store for grounded RAG applications.

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Connect Fauna (Serverless DB) MCP to LlamaIndex

Create your Vinkius account to connect Fauna (Serverless DB) to LlamaIndex 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 LlamaIndex

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Index live Fauna data with this MCP Server

The `execute_fql` tool lets your LlamaIndex agent fetch documents directly from your database to build a searchable knowledge base. Instead of querying static files, your agent pulls live production records and indexes them dynamically into your vector stores. This ensures your retrieval-augmented generation pipelines always use fresh data. You can write queries that target specific collections or indexes. The agent processes the raw JSON output, converts it into document nodes, and makes it instantly searchable for your LLM.

Ground RAG pipelines in actual database records

Your LlamaIndex agent uses this MCP Server to query your database with `execute_fql` before answering user prompts. This prevents hallucinations by grounding the agent's responses in real-time serverless database records. If a user asks about their account status, the agent pulls the exact record to answer. The agent determines which queries to run based on the user's intent. By combining live database lookups with semantic search over your vector index, you get highly accurate answers that reflect the current state of your application.

Query past database sessions semantically

This integration allows you to index the outputs of the `execute_fql` tool into a persistent vector store. Your LlamaIndex agent can then run semantic searches over previous query results to find patterns or historical data. You don't have to keep querying the live database for the same static information. Query costs and database load drop significantly when you cache results this way. The agent simply checks the local vector index first, only hitting Fauna when it detects that the cached data is stale or incomplete.

Setup guide

Set up Fauna (Serverless DB) MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Fauna (Serverless DB) MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Fauna (Serverless DB) tools.",
)
response = await agent.run("List recent Fauna (Serverless DB) data")

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Common questions about Fauna (Serverless DB) MCP in LlamaIndex

Instantiate the basic client with your Vinkius HTTP URL and wrap it in the tool spec class. From there, convert it to a tool list and pass it directly to your LlamaIndex function agent.

Yes, the output from the `execute_fql` tool is returned as structured JSON which LlamaIndex can parse into document nodes. These nodes are then ingested and indexed into your vector store for semantic search.

By providing a direct line to your database via FQL, the agent can fetch ground-truth records before generating a response. This ensures the engine relies on actual database state rather than training data.

Yes, you can use the allowed tools filter during setup to restrict the agent's access. This ensures the agent only executes the specific queries you want to permit in that session.

Connection tokens are managed securely by Vinkius and injected into the ephemeral execution context. Your raw database credentials and FQL query payloads are never logged or exposed to third-party services.

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