How to Use the Neon MCP in LlamaIndex

Q: How does LlamaIndex know which Neon project to query?

You inject the output of listprojects into your index via the MCP integration. The agent searches the embedded documents to find the matching project ID before calling other tools.

Q: Can I use LlamaIndex to update my Neon project names?

Yes. The agent uses the updateproject tool to rename a workspace. It then updates the vector store to reflect the new nomenclature.

Q: Will indexing my branch history consume too many tokens?

It depends on your branch volume. The listbranches tool returns metadata like parent IDs and creation dates, which is mostly lightweight text.

Q: Can the agent create a new user role for a specific database?

It calls createrole with the target project and branch IDs. The agent retrieves the generated password and stores it in the session context.

Index your Neon infrastructure directly into LlamaIndex. Build RAG applications that query live database configurations and active branches.

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Query live Neon environments

The `list_projects` tool pulls your entire serverless workspace hierarchy, including regions and Postgres versions. Your LlamaIndex agent ingests this metadata and embeds it into your vector store. You ask your agent which projects are running Postgres 17, and it checks the actual indexed state rather than guessing. This extends to granular resources. By running `list_branches` and `list_databases`, the agent indexes the creation dates and parent IDs of every isolated environment. You get a searchable knowledge base of your exact database topology.

Provision branches via MCP Server

The `create_branch` tool clones your primary database instantly with zero data copy overhead. When a user asks to test a new schema, the agent searches the index to find the correct project ID, then executes the creation. After the branch spins up, the agent runs `get_connection_uri`. It returns the exact host and role needed to connect, updating your LlamaIndex knowledge base with the fresh endpoint data so subsequent queries know the branch exists.

Track endpoint states in LlamaIndex

The `list_endpoints` tool retrieves the active, idle, or suspended status of your compute nodes. Your agent embeds these states into your index, creating a historical record of autoscaling configurations across different branches. If someone asks why a staging environment is slow, the agent pulls the endpoint data. It identifies if the branch only has a read-only endpoint or if the read-write node is currently suspended.

Setup guide

Set up Neon 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 Neon 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 Neon tools.",
)
response = await agent.run("List recent Neon data")

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Common questions about Neon MCP in LlamaIndex

You inject the output of `list_projects` into your index via the MCP integration. The agent searches the embedded documents to find the matching project ID before calling other tools.

Yes. The agent uses the `update_project` tool to rename a workspace. It then updates the vector store to reflect the new nomenclature.

It depends on your branch volume. The `list_branches` tool returns metadata like parent IDs and creation dates, which is mostly lightweight text.

It calls `create_role` with the target project and branch IDs. The agent retrieves the generated password and stores it in the session context.

The server retrieves host addresses, role names, and passwords via `get_connection_uri`. Because this MCP integration runs on a zero-trust, stateless architecture, connection details only persist if you explicitly choose to embed them into your own storage layer.

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