Can my AI agent directly run RAG without calling external LLMs?

Yes! Use the `retrieve_and_generate` capability. Your agent passes the query and a designated Bedrock model ARN. Bedrock handles fetching chunks from the local vector index and synthesizing the final answer inside AWS boundaries, returning a fully grounded response instantly.

How can I check if new uploaded documents are successfully indexed in my agent?

Just ask your agent to list ingestion jobs for a specific Knowledge Base ID and Data Source ID. It will report back the exact status (e.g., SYNCING, COMPLETED, FAILED) of chunks being mapped to your vector layout.

Can I see exactly where an answer came from in my documentation?

Absolutely. Both the standard `retrieve` functionality and `retrieve_and_generate` calls will parse out the specific origin document URLs (e.g., S3 paths) and expose the exact raw text snippets that mathematically matched your query vector.

Amazon Bedrock KB MCP Server for OpenAI Agents SDK 6 tools — connect in under 2 minutes

Name: Amazon Bedrock KB
Availability: InStock
Author: Vinkius

openai.github.io/openai-agents-python/mcp

Built by Vinkius GDPR 6 Tools SDK

The OpenAI Agents SDK enables production-grade agent workflows in Python. Connect Amazon Bedrock KB through the Vinkius and your agents gain typed, auto-discovered tools with built-in guardrails — no manual schema definitions required.

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

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="Amazon Bedrock KB Assistant",
            instructions=(
                "You help users interact with Amazon Bedrock KB. "
                "You have access to 6 tools."
            ),
            mcp_servers=[mcp_server],
        )

        result = await Runner.run(
            agent, "List all available tools from Amazon Bedrock KB"
        )
        print(result.final_output)

asyncio.run(main())

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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 Amazon Bedrock KB MCP Server

Connect your Amazon Bedrock account to any AI agent and empower it with managed vector databases, enterprise RAG workflows, and semantic search directly inside AWS.

The OpenAI Agents SDK auto-discovers all 6 tools from Amazon Bedrock KB through native MCP integration. Build agents with built-in guardrails, tracing, and handoff patterns — chain multiple agents where one queries Amazon Bedrock KB, another analyzes results, and a third generates reports, all orchestrated through the Vinkius.

What you can do

Managed RAG — Generate grounded LLM responses using internal document sets in a single explicit call
Semantic Retrieval — Query vector indexes to retrieve exact top-K text chunks and their origin document URLs
Data Sources — Inspect and paginate attached storage buckets feeding the knowledge base
Ingestion Jobs — Track real-time syncing status of chunking pipelines mapping documents across the vector layout
Knowledge Base Introspection — List available vector stores and exact embedding models assigned directly to your region

The Amazon Bedrock KB 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 Amazon Bedrock KB to OpenAI Agents SDK via MCP

Follow these steps to integrate the Amazon Bedrock KB 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 Amazon Bedrock KB

Why Use OpenAI Agents SDK with the Amazon Bedrock KB MCP Server

OpenAI Agents SDK provides unique advantages when paired with Amazon Bedrock KB 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

Amazon Bedrock KB + OpenAI Agents SDK Use Cases

Practical scenarios where OpenAI Agents SDK combined with the Amazon Bedrock KB MCP Server delivers measurable value.

Automated workflows: build agents that query Amazon Bedrock KB, process the data, and trigger follow-up actions autonomously

Multi-agent orchestration: create specialist agents — one queries Amazon Bedrock KB, another analyzes results, a third generates reports

Data enrichment pipelines: stream data through Amazon Bedrock KB tools and transform it with OpenAI models in a single async loop

Customer support bots: agents query Amazon Bedrock KB to resolve tickets, look up records, and update statuses without human intervention

Amazon Bedrock KB MCP Tools for OpenAI Agents SDK (6)

These 6 tools become available when you connect Amazon Bedrock KB to OpenAI Agents SDK via MCP:

get_knowledge_base

Get an explicit AWS Bedrock knowledge base

list_data_sources

List Data Sources bound explicitly to an AWS Bedrock KB

list_ingestion_jobs

List AWS Bedrock KB explicit sync operations

list_knowledge_bases

List AWS Bedrock knowledge bases

retrieve

Query a vector index securely via AWS Bedrock

retrieve_and_generate

Generate explicitly grounded LLM responses using Bedrock KB

Example Prompts for Amazon Bedrock KB in OpenAI Agents SDK

Ready-to-use prompts you can give your OpenAI Agents SDK agent to start working with Amazon Bedrock KB immediately.

"Which knowledge bases and embedding models do I have setup?"

"Run a retrieval query for 'onboarding process checklist' on my KB and show me the top 3 snippets."

"Check the status of the S3 ingestion job for my Documentation bucket."

Troubleshooting Amazon Bedrock KB MCP Server with OpenAI Agents SDK

Common issues when connecting Amazon Bedrock KB to OpenAI Agents SDK through the Vinkius, and how to resolve them.

MCPServerStreamableHttp not found

Ensure you have the latest version: pip install --upgrade openai-agents

Agent not calling tools

Make sure your prompt explicitly references the task the tools can help with.

Amazon Bedrock KB + OpenAI Agents SDK FAQ

Common questions about integrating Amazon Bedrock KB MCP Server with OpenAI Agents SDK.

How does the OpenAI Agents SDK connect to MCP?

Use MCPServerSse(url=...) to create a server connection. The SDK auto-discovers all tools and makes them available to your agent with full type information.