Amazon Bedrock KB MCP Server for AutoGen 6 tools — connect in under 2 minutes
Microsoft AutoGen enables multi-agent conversations where agents negotiate, delegate, and execute tasks collaboratively. Add Amazon Bedrock KB as an MCP tool provider through the Vinkius and every agent in the group can access live data and take action.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.tools.mcp import McpWorkbench
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
async with McpWorkbench(
server_params={"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"},
transport="streamable_http",
) as workbench:
tools = await workbench.list_tools()
agent = AssistantAgent(
name="amazon_bedrock_kb_agent",
tools=tools,
system_message=(
"You help users with Amazon Bedrock KB. "
"6 tools available."
),
)
print(f"Agent ready with {len(tools)} tools")
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 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.
AutoGen enables multi-agent conversations where agents negotiate, delegate, and collaboratively use Amazon Bedrock KB tools. Connect 6 tools through the Vinkius and assign role-based access — a data analyst queries while a reviewer validates, with optional human-in-the-loop approval for sensitive operations.
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 AutoGen 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 AutoGen via MCP
Follow these steps to integrate the Amazon Bedrock KB MCP Server with AutoGen.
Install AutoGen
Run pip install "autogen-ext[mcp]"
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Integrate into workflow
Use the agent in your AutoGen multi-agent orchestration
Explore tools
The workbench discovers 6 tools from Amazon Bedrock KB automatically
Why Use AutoGen with the Amazon Bedrock KB MCP Server
AutoGen provides unique advantages when paired with Amazon Bedrock KB through the Model Context Protocol.
Multi-agent conversations: multiple AutoGen agents discuss, delegate, and collaboratively use Amazon Bedrock KB tools to solve complex tasks
Role-based architecture lets you assign Amazon Bedrock KB tool access to specific agents — a data analyst queries while a reviewer validates
Human-in-the-loop support: agents can pause for human approval before executing sensitive Amazon Bedrock KB tool calls
Code execution sandbox: AutoGen agents can write and run code that processes Amazon Bedrock KB tool responses in an isolated environment
Amazon Bedrock KB + AutoGen Use Cases
Practical scenarios where AutoGen combined with the Amazon Bedrock KB MCP Server delivers measurable value.
Collaborative analysis: one agent queries Amazon Bedrock KB while another validates results and a third generates the final report
Automated review pipelines: a researcher agent fetches data from Amazon Bedrock KB, a critic agent evaluates quality, and a writer produces the output
Interactive planning: agents negotiate task allocation using Amazon Bedrock KB data to make informed decisions about resource distribution
Code generation with live data: an AutoGen coder agent writes scripts that process Amazon Bedrock KB responses in a sandboxed execution environment
Amazon Bedrock KB MCP Tools for AutoGen (6)
These 6 tools become available when you connect Amazon Bedrock KB to AutoGen 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 AutoGen
Ready-to-use prompts you can give your AutoGen 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 AutoGen
Common issues when connecting Amazon Bedrock KB to AutoGen through the Vinkius, and how to resolve them.
McpWorkbench not found
pip install "autogen-ext[mcp]"Amazon Bedrock KB + AutoGen FAQ
Common questions about integrating Amazon Bedrock KB MCP Server with AutoGen.
How does AutoGen connect to MCP servers?
Can different agents have different MCP tool access?
Does AutoGen support human approval for tool calls?
Connect Amazon Bedrock KB with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
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Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Amazon Bedrock KB to AutoGen
Get your token, paste the configuration, and start using 6 tools in under 2 minutes. No API key management needed.