Marqo AI (Vector Search & Embeddings) MCP Server for LlamaIndex 6 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Marqo AI (Vector Search & Embeddings) as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
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 Marqo AI (Vector Search & Embeddings). "
"You have 6 tools available."
),
)
response = await agent.run(
"What tools are available in Marqo AI (Vector Search & Embeddings)?"
)
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 Marqo AI (Vector Search & Embeddings) MCP Server
Connect your Marqo instance to any AI agent and take full control of your semantic search infrastructure, vector embeddings, and real-time document indexing through natural conversation.
LlamaIndex agents combine Marqo AI (Vector Search & Embeddings) tool responses with indexed documents for comprehensive, grounded answers. Connect 6 tools through the Vinkius and query live data alongside vector stores and SQL databases in a single turn — ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Tensor Search Orchestration — Execute dense semantic similarity searches against your indices using natural language queries, with Marqo handling embedding extraction automatically
- Dynamic Document Ingestion — Write new JSON records into your vector indices directly from your agent, allowing for instant searchability of fresh data mappings
- Index Lifecycle Management — Create explicitly bounded new vector indices with custom model settings and dimension constraints to optimize your search architecture
- Vector Audit & Stats — Retrieve detailed configuration metrics for your indices, including document counts, embedding model types, and underlying schema mappings
- Precision Deletion — Physically eradicate vectorized representations by targeting specific scalar identifiers to maintain a clean and relevant search index
- Resource Inventory — List all available vector indices on your Marqo instance to identify collection boundaries before executing search queries
The Marqo AI (Vector Search & Embeddings) MCP Server exposes 6 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.
How to Connect Marqo AI (Vector Search & Embeddings) to LlamaIndex via MCP
Follow these steps to integrate the Marqo AI (Vector Search & Embeddings) MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 6 tools from Marqo AI (Vector Search & Embeddings)
Why Use LlamaIndex with the Marqo AI (Vector Search & Embeddings) MCP Server
LlamaIndex provides unique advantages when paired with Marqo AI (Vector Search & Embeddings) through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Marqo AI (Vector Search & Embeddings) tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Marqo AI (Vector Search & Embeddings) tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Marqo AI (Vector Search & Embeddings), a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Marqo AI (Vector Search & Embeddings) tools were called, what data was returned, and how it influenced the final answer
Marqo AI (Vector Search & Embeddings) + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Marqo AI (Vector Search & Embeddings) MCP Server delivers measurable value.
Hybrid search: combine Marqo AI (Vector Search & Embeddings) real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Marqo AI (Vector Search & Embeddings) 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 Marqo AI (Vector Search & Embeddings) for fresh data
Analytical workflows: chain Marqo AI (Vector Search & Embeddings) queries with LlamaIndex's data connectors to build multi-source analytical reports
Marqo AI (Vector Search & Embeddings) MCP Tools for LlamaIndex (6)
These 6 tools become available when you connect Marqo AI (Vector Search & Embeddings) to LlamaIndex via MCP:
add_documents
Write new documents into Marqo
create_index
Create an explicitly bounded new vector index
delete_documents
Delete specific documents from Marqo by targeting their IDs
get_index_stats
Get configuration and stats for an index
list_indexes
Crucial before writing queries hitting arbitrary collections. List all Marqo vector indexes
tensor_search
Perform natural language tensor search on Marqo
Example Prompts for Marqo AI (Vector Search & Embeddings) in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Marqo AI (Vector Search & Embeddings) immediately.
"Semantic search in index 'products' for 'lightweight running shoes for trails'"
"List all vector indexes in my Marqo instance"
"Add this document to the 'support-docs' index: {"title": "API Auth", "content": "Use Marqo-API-Key header"}"
Troubleshooting Marqo AI (Vector Search & Embeddings) MCP Server with LlamaIndex
Common issues when connecting Marqo AI (Vector Search & Embeddings) to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpMarqo AI (Vector Search & Embeddings) + LlamaIndex FAQ
Common questions about integrating Marqo AI (Vector Search & Embeddings) MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Marqo AI (Vector Search & Embeddings) with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Marqo AI (Vector Search & Embeddings) to LlamaIndex
Get your token, paste the configuration, and start using 6 tools in under 2 minutes. No API key management needed.