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 LlamaIndex 6 tools — connect in under 2 minutes

Name: Amazon Bedrock KB
Availability: InStock
Author: Vinkius

docs.llamaindex.ai/en/stable/module_guides/tools/mcp

Built by Vinkius GDPR 6 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Amazon Bedrock KB as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

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 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 Amazon Bedrock KB. "
            "You have 6 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in Amazon Bedrock KB?"
    )
    print(response)

asyncio.run(main())

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* 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.

LlamaIndex agents combine Amazon Bedrock KB 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

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 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 Amazon Bedrock KB to LlamaIndex via MCP

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

Why Use LlamaIndex with the Amazon Bedrock KB MCP Server

LlamaIndex provides unique advantages when paired with Amazon Bedrock KB through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine Amazon Bedrock KB tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain Amazon Bedrock KB tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query Amazon Bedrock KB, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what Amazon Bedrock KB tools were called, what data was returned, and how it influenced the final answer

Amazon Bedrock KB + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the Amazon Bedrock KB MCP Server delivers measurable value.

Hybrid search: combine Amazon Bedrock KB real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query Amazon Bedrock KB 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 Amazon Bedrock KB for fresh data

Analytical workflows: chain Amazon Bedrock KB queries with LlamaIndex's data connectors to build multi-source analytical reports

Amazon Bedrock KB MCP Tools for LlamaIndex (6)

These 6 tools become available when you connect Amazon Bedrock KB to LlamaIndex 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 LlamaIndex

Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex

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

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Amazon Bedrock KB + LlamaIndex FAQ

Common questions about integrating Amazon Bedrock KB MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query Amazon Bedrock KB tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.