GroundX MCP Server for LlamaIndexGive LlamaIndex instant access to 12 tools to Create Bucket, Create Group, Get Customer Info, and more
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add GroundX as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
Ask AI about this App Connector for LlamaIndex
The GroundX app connector for LlamaIndex is a standout in the Knowledge Management category — giving your AI agent 12 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to GroundX. "
"You have 12 tools available."
),
)
response = await agent.run(
"What tools are available in GroundX?"
)
print(response)
asyncio.run(main())
* 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 GroundX MCP Server
The GroundX MCP server enables your AI agent to search across enterprise data stores and manage RAG (Retrieval-Augmented Generation) pipelines, retrieving highly relevant document chunks seamlessly.
LlamaIndex agents combine GroundX tool responses with indexed documents for comprehensive, grounded answers. Connect 12 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
The GroundX MCP Server exposes 12 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
All 12 GroundX tools available for LlamaIndex
When LlamaIndex connects to GroundX through Vinkius, your AI agent gets direct access to every tool listed below — spanning rag-as-a-service, data-search, document-retrieval, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.
Create a new bucket
Create a new group
Retrieve account and customer details
Check the processing status of an ingestion task
Ingest documents into GroundX from URLs or local paths
Crawl and ingest content from a website URL
List all buckets (containers for documents)
List all ingested documents
List all groups (aggregations of buckets)
List all RAG workflows
Perform semantic search across all content
Search for specific documents based on metadata or content
Connect GroundX to LlamaIndex via MCP
Follow these steps to wire GroundX into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install dependencies
pip install llama-index-tools-mcp llama-index-llms-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
Why Use LlamaIndex with the GroundX MCP Server
LlamaIndex provides unique advantages when paired with GroundX through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine GroundX tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain GroundX tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query GroundX, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what GroundX tools were called, what data was returned, and how it influenced the final answer
GroundX + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the GroundX MCP Server delivers measurable value.
Hybrid search: combine GroundX real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query GroundX to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying GroundX for fresh data
Analytical workflows: chain GroundX queries with LlamaIndex's data connectors to build multi-source analytical reports
Example Prompts for GroundX in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with GroundX immediately.
"List all my GroundX data buckets."
"Search for 'refund policy' in bucket 102."
"Check the document count in bucket 101."
Troubleshooting GroundX MCP Server with LlamaIndex
Common issues when connecting GroundX to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpGroundX + LlamaIndex FAQ
Common questions about integrating GroundX MCP Server with LlamaIndex.