OpenSearch Vector MCP Server for OpenAI Agents SDK 6 tools — connect in under 2 minutes
The OpenAI Agents SDK enables production-grade agent workflows in Python. Connect OpenSearch Vector through Vinkius and your agents gain typed, auto-discovered tools with built-in guardrails. no manual schema definitions required.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerStreamableHttp
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
async with MCPServerStreamableHttp(
url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
) as mcp_server:
agent = Agent(
name="OpenSearch Vector Assistant",
instructions=(
"You help users interact with OpenSearch Vector. "
"You have access to 6 tools."
),
mcp_servers=[mcp_server],
)
result = await Runner.run(
agent, "List all available tools from OpenSearch Vector"
)
print(result.final_output)
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.
The OpenAI Agents SDK auto-discovers all 6 tools from OpenSearch Vector through native MCP integration. Build agents with built-in guardrails, tracing, and handoff patterns. chain multiple agents where one queries OpenSearch Vector, another analyzes results, and a third generates reports, all orchestrated through Vinkius.
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 OpenAI Agents SDK 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 OpenAI Agents SDK via MCP
Follow these steps to integrate the OpenSearch Vector MCP Server with OpenAI Agents SDK.
Install the SDK
Run pip install openai-agents in your Python environment
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com
Run the script
Save the code above and run it: python agent.py
Explore tools
The agent will automatically discover 6 tools from OpenSearch Vector
Why Use OpenAI Agents SDK with the OpenSearch Vector MCP Server
OpenAI Agents SDK provides unique advantages when paired with OpenSearch Vector through the Model Context Protocol.
Native MCP integration via `MCPServerSse`, pass the URL and the SDK auto-discovers all tools with full type safety
Built-in guardrails, tracing, and handoff patterns let you build production-grade agents without reinventing safety infrastructure
Lightweight and composable: chain multiple agents and MCP servers in a single pipeline with minimal boilerplate
First-party OpenAI support ensures optimal compatibility with GPT models for tool calling and structured output
OpenSearch Vector + OpenAI Agents SDK Use Cases
Practical scenarios where OpenAI Agents SDK combined with the OpenSearch Vector MCP Server delivers measurable value.
Automated workflows: build agents that query OpenSearch Vector, process the data, and trigger follow-up actions autonomously
Multi-agent orchestration: create specialist agents. one queries OpenSearch Vector, another analyzes results, a third generates reports
Data enrichment pipelines: stream data through OpenSearch Vector tools and transform it with OpenAI models in a single async loop
Customer support bots: agents query OpenSearch Vector to resolve tickets, look up records, and update statuses without human intervention
OpenSearch Vector MCP Tools for OpenAI Agents SDK (6)
These 6 tools become available when you connect OpenSearch Vector to OpenAI Agents SDK 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 OpenAI Agents SDK
Ready-to-use prompts you can give your OpenAI Agents SDK 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 OpenAI Agents SDK
Common issues when connecting OpenSearch Vector to OpenAI Agents SDK through the Vinkius, and how to resolve them.
MCPServerStreamableHttp not found
pip install --upgrade openai-agentsAgent not calling tools
OpenSearch Vector + OpenAI Agents SDK FAQ
Common questions about integrating OpenSearch Vector MCP Server with OpenAI Agents SDK.
How does the OpenAI Agents SDK connect to MCP?
MCPServerSse(url=...) to create a server connection. The SDK auto-discovers all tools and makes them available to your agent with full type information.Can I use multiple MCP servers in one agent?
MCPServerSse instances to the agent constructor. The agent can use tools from all connected servers within a single run.Does the SDK support streaming responses?
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 OpenAI Agents SDK
Get your token, paste the configuration, and start using 6 tools in under 2 minutes. No API key management needed.