Can I query my indexed documents using natural language through my agent?

Yes. Use the `query_pipeline` tool by providing the Pipeline ID and your natural language question. Your agent will trigger a real-time RAG extraction and return a synthesized answer based on the relevant source documents found in the index.

How do I check which files have been successfully ingested into a pipeline?

The `list_files` tool allows your agent to retrieve explicit metadata for all physical documents attached to a pipeline. This is perfect for auditing your data source boundaries and ensuring all required documents are correctly indexed.

Can my agent manage multiple semantic indices?

Absolutely. Use the `list_indexes` tool to see all active semantic stores managed by LlamaCloud. Your agent will report the index names and types, making it easy to identify the correct target for your search or ingestion workflows.

LlamaIndex (AI Data Framework & RAG) MCP Server for Google ADK 6 tools — connect in under 2 minutes

Name: LlamaIndex (AI Data Framework & RAG)
Availability: InStock
Author: Vinkius

google.github.io/adk-docs/tools/mcp-tools

Built by Vinkius GDPR 6 Tools SDK

Google Agent Development Kit (ADK) is Google's framework for building production AI agents. Add LlamaIndex (AI Data Framework & RAG) as an MCP tool provider through the Vinkius and your ADK agents can call every tool with full schema introspection.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

from google.adk.agents import Agent
from google.adk.tools.mcp_tool import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import (
    StreamableHTTPConnectionParams,
)

# Your Vinkius token — get it at cloud.vinkius.com
mcp_tools = McpToolset(
    connection_params=StreamableHTTPConnectionParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    )
)

agent = Agent(
    model="gemini-2.5-pro",
    name="llamaindex_ai_data_framework_rag_agent",
    instruction=(
        "You help users interact with LlamaIndex (AI Data Framework & RAG) "
        "using 6 available tools."
    ),
    tools=[mcp_tools],
)

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About LlamaIndex (AI Data Framework & RAG) MCP Server

Connect your LlamaIndex (LlamaCloud) account to any AI agent and take full control of your RAG data framework and semantic search orchestration through natural conversation.

Google ADK natively supports LlamaIndex (AI Data Framework & RAG) as an MCP tool provider — declare the Vinkius Edge URL and the framework handles discovery, validation, and execution automatically. Combine 6 tools with Gemini's long-context reasoning for complex multi-tool workflows, with production-ready session management and evaluation built in.

What you can do

RAG Orchestration — Execute structural natural language queries directly against your data pipelines to retrieve synthesized answers grounded in your source documents
Index Visibility — List managed active indices wrapping your semantic stores and verify how your data is distributed across indexed databases
File Audit — Retrieve explicit metadata for raw source files currently ingested by your pipelines to verify document tracking and ingestion limits
Pipeline Management — List deployed data pipelines and retrieve detailed configurations including connected sources and embedding settings directly from your agent
Project CRM — Navigate across high-level LlamaIndex projects managing collections of pipelines and queryable semantic search boundaries securely
Real-time Synthesis — Use your agent to perform real-time RAG extraction, ensuring your AI workflows are powered by accurate, indexed enterprise knowledge

The LlamaIndex (AI Data Framework & RAG) MCP Server exposes 6 tools through the Vinkius. Connect it to Google ADK in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect LlamaIndex (AI Data Framework & RAG) to Google ADK via MCP

Follow these steps to integrate the LlamaIndex (AI Data Framework & RAG) MCP Server with Google ADK.

Install Google ADK

Run pip install google-adk

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Create the agent

Save the code above and integrate into your ADK workflow

Explore tools

The agent will discover 6 tools from LlamaIndex (AI Data Framework & RAG) via MCP

Why Use Google ADK with the LlamaIndex (AI Data Framework & RAG) MCP Server

Google ADK provides unique advantages when paired with LlamaIndex (AI Data Framework & RAG) through the Model Context Protocol.

Google ADK natively supports MCP tool servers — declare a tool provider and the framework handles discovery, validation, and execution

Built on Gemini models, ADK provides long-context reasoning ideal for complex multi-tool workflows with LlamaIndex (AI Data Framework & RAG)

Production-ready features like session management, evaluation, and deployment come built-in — not bolted on

Seamless integration with Google Cloud services means you can combine LlamaIndex (AI Data Framework & RAG) tools with BigQuery, Vertex AI, and Cloud Functions

LlamaIndex (AI Data Framework & RAG) + Google ADK Use Cases

Practical scenarios where Google ADK combined with the LlamaIndex (AI Data Framework & RAG) MCP Server delivers measurable value.

Enterprise data agents: ADK agents query LlamaIndex (AI Data Framework & RAG) and cross-reference results with internal databases for comprehensive analysis

Multi-modal workflows: combine LlamaIndex (AI Data Framework & RAG) tool responses with Gemini's vision and language capabilities in a single agent

Automated compliance checks: schedule ADK agents to query LlamaIndex (AI Data Framework & RAG) regularly and flag policy violations or configuration drift

Internal tool platforms: build self-service agent platforms where teams connect their own MCP servers including LlamaIndex (AI Data Framework & RAG)

LlamaIndex (AI Data Framework & RAG) MCP Tools for Google ADK (6)

These 6 tools become available when you connect LlamaIndex (AI Data Framework & RAG) to Google ADK via MCP:

get_pipeline

Get configuration details for a specific pipeline

list_files

List raw source files currently ingested by a pipeline

list_indexes

List LlamaCloud active indexes

list_pipelines

List LlamaCloud deployed data pipelines

list_projects

List active LlamaCloud projects

query_pipeline

Execute a natural language query against a specific Pipeline

Example Prompts for LlamaIndex (AI Data Framework & RAG) in Google ADK

Ready-to-use prompts you can give your Google ADK agent to start working with LlamaIndex (AI Data Framework & RAG) immediately.

"Query the 'Product-Docs' pipeline about 'multi-tenant security architecture'"

"List all files ingested by the 'Engineering-Handbook' pipeline (ID: pipe-123)"

"What are the active LlamaCloud projects in our organization?"

Troubleshooting LlamaIndex (AI Data Framework & RAG) MCP Server with Google ADK

Common issues when connecting LlamaIndex (AI Data Framework & RAG) to Google ADK through the Vinkius, and how to resolve them.

McpToolset not found

Update: pip install --upgrade google-adk

LlamaIndex (AI Data Framework & RAG) + Google ADK FAQ

Common questions about integrating LlamaIndex (AI Data Framework & RAG) MCP Server with Google ADK.

How does Google ADK connect to MCP servers?

Import the MCP toolset class and pass the server URL. ADK discovers and registers all tools automatically, making them available to your agent's tool-use loop.

Can ADK agents use multiple MCP servers?

Yes. Declare multiple MCP tool providers in your agent configuration. ADK merges all tool schemas and the agent can call tools from any server in a single turn.

Which Gemini models work best with MCP tools?

Gemini 2.0 Flash and Pro models both support function calling required for MCP tools. Flash is recommended for latency-sensitive use cases, Pro for complex reasoning.