OpenSearch Vector 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 OpenSearch Vector 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 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 OpenSearch Vector. "
"You have 6 tools available."
),
)
response = await agent.run(
"What tools are available in OpenSearch Vector?"
)
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 OpenSearch Vector MCP Server
Turn your OpenSearch cluster into an AI-native vector database. Create k-NN indexes, upsert embeddings, run similarity searches, and inspect index configurations — all through natural conversation with your AI agent.
LlamaIndex agents combine OpenSearch Vector tool responses with indexed documents for comprehensive, grounded answers. Connect 6 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.
What you can do
- Vector Search — Execute k-Nearest Neighbors queries against any k-NN index with custom top-K limits and dense float vectors
- Index Management — List all cluster indexes with health status and document counts, or inspect a specific index's vector dimension, engine config, and distance metric
- Create Index — Provision new k-NN indexes optimized for cosine similarity with configurable vector dimensions (384, 768, 1536, etc.)
- Document Operations — Upsert vector documents with metadata, or delete documents from the embedding space by ID
The OpenSearch Vector 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 OpenSearch Vector to LlamaIndex via MCP
Follow these steps to integrate the OpenSearch Vector 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 OpenSearch Vector
Why Use LlamaIndex with the OpenSearch Vector MCP Server
LlamaIndex provides unique advantages when paired with OpenSearch Vector through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine OpenSearch Vector tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain OpenSearch Vector tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query OpenSearch Vector, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what OpenSearch Vector tools were called, what data was returned, and how it influenced the final answer
OpenSearch Vector + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the OpenSearch Vector MCP Server delivers measurable value.
Hybrid search: combine OpenSearch Vector real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query OpenSearch Vector 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 OpenSearch Vector for fresh data
Analytical workflows: chain OpenSearch Vector queries with LlamaIndex's data connectors to build multi-source analytical reports
OpenSearch Vector MCP Tools for LlamaIndex (6)
These 6 tools become available when you connect OpenSearch Vector to LlamaIndex via MCP:
create_index
knn: true` and mapping a rigid dynamic dense vector field optimized for cosine similarity. Create a new native OpenSearch KNN index ready for vector embeddings
delete_document
Delete an explicit vector document bounding from OpenSearch
get_index
Retrieve explicit OpenSearch index mapping and settings
index_document
This executes a fast transactional atomic insertion into the embedding space. Upsert a singular vector document directly into an OpenSearch KNN index
list_indexes
List all explicit indexes residing on the OpenSearch cluster
search
Provide the exact index name and a JSON-stringified dense float vector array to find conceptually similar embeddings natively. Execute a K-Nearest Neighbors (k-NN) vector search against OpenSearch
Example Prompts for OpenSearch Vector in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with OpenSearch Vector immediately.
"List all vector indexes in my OpenSearch cluster."
"Find the 5 most similar documents to this embedding in the knowledge-base index."
"Create a new k-NN index called 'customer-feedback' with 1536 dimensions."
Troubleshooting OpenSearch Vector MCP Server with LlamaIndex
Common issues when connecting OpenSearch Vector to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpOpenSearch Vector + LlamaIndex FAQ
Common questions about integrating OpenSearch Vector 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 OpenSearch Vector 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 OpenSearch Vector to LlamaIndex
Get your token, paste the configuration, and start using 6 tools in under 2 minutes. No API key management needed.