Bring Rag
to Google ADK
Create your Vinkius account to connect Amazon Bedrock KB to Google ADK and start using all 6 AI tools in minutes. Fully managed, enterprise secure, and ready to use without writing a single line of code. No hosting, no server setup — just connect and start using.
GDPR Free for SubscribersCompatible with every major AI agent and IDE
Gemini
What is the 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.
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
How it works
- Subscribe to this server
- Enter your AWS IAM Role/User Access Credentials
- Start augmenting your agent's context from Claude, Cursor, or any MCP-compatible client
Eliminate the need to build custom vector pipelines. Your agent queries massive corporate datasets precisely where they reside in AWS.
Who is this for?
- AI Developers — build RAG workflows rapidly without hosting databases or maintaining chunking sync logic
- Cloud Architects — audit ingestion status and check origin document mappings securely from your chat interface
- Data Scientists — prototype context-grounded queries instantly and trace accuracy against exact data chunks
Built-in capabilities (6)
Get an explicit AWS Bedrock knowledge base
List Data Sources bound explicitly to an AWS Bedrock KB
List AWS Bedrock KB explicit sync operations
List AWS Bedrock knowledge bases
Query a vector index securely via AWS Bedrock
Generate explicitly grounded LLM responses using Bedrock KB
Why Google ADK?
Google ADK natively supports Amazon Bedrock KB as an MCP tool provider. declare Vinkius Edge URL and the framework handles discovery, validation, and execution automatically. Combine 6 tools with Gemini's long-context reasoning for complex multi-tool workflows, with production-ready session management and evaluation built in.
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Google ADK natively supports MCP tool servers. declare a tool provider and the framework handles discovery, validation, and execution
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Built on Gemini models, ADK provides long-context reasoning ideal for complex multi-tool workflows with Amazon Bedrock KB
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Production-ready features like session management, evaluation, and deployment come built-in. not bolted on
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Seamless integration with Google Cloud services means you can combine Amazon Bedrock KB tools with BigQuery, Vertex AI, and Cloud Functions
Amazon Bedrock KB in Google ADK
Why run Amazon Bedrock KB with Vinkius?
The Amazon Bedrock KB connection runs on our fully managed, secure cloud infrastructure. We handle the hosting, maintenance, and security so you don't have to deal with servers or code. All 6 tools are ready to work instantly without any complex setup.
You stay in complete control of your data. Your AI only accesses the information you approve, keeping your sensitive passwords and private details completely safe. Plus, with automatic optimizations, your AI works faster and more efficiently.
* Every connection is hosted and maintained by Vinkius. We handle the security, updates, and infrastructure so you don't have to write code or manage servers. See our infrastructure
Over 4,000 integrations ready for AI agents
Explore a vast library of pre-built integrations, optimized and ready to deploy.
Connect securely in under 30 seconds
Generate tokens to authenticate and link external services in a single step.
Complete visibility into every agent action
Audit live requests, latency, success rates, and active security compliance policies.
Optimize spending and track token ROI
Analyze real-time token consumption and cost metrics detailed by connection.
Explore our live AI Agents Analytics dashboard to see it all working
This dashboard is included when you connect Amazon Bedrock KB using Vinkius. You will never be left in the dark about what your AI agents are doing with your tools.
Amazon Bedrock KB and 4,000+ other AI tools. No hosting, no code, ready to use.
Professionals who connect Amazon Bedrock KB to Google ADK through Vinkius don't need to write code, manage servers, or worry about security. Everything is pre-configured, secure, and runs automatically in the background.
Raw MCP | Vinkius | |
|---|---|---|
| Ready-to-use MCPs | Find and configure each manually | 4,000+ MCPs ready to use |
| Connection Setup | Manual coding & server setup | 1-click instant connection |
| Server Hosting | You host it yourself (needs 24/7 uptime) | 100% hosted & managed by Vinkius |
| Security & Privacy | Stored in plaintext config files | Bank-grade encrypted vault |
| Activity Visibility | Blind execution (no logs or tracking) | Live dashboard with real-time logs |
| Cost Control | Runaway AI token spend risk | Automatic budget limits |
| Revoking Access | Must delete files or code to stop | 1-click disconnect button |
How Vinkius secures
Amazon Bedrock KB for Google ADK
Every request between Google ADK and Amazon Bedrock KB is protected by our secure gateway. We automatically keep your sensitive data private, prevent unauthorized access, and let you disconnect instantly at any time.
Frequently asked questions
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.
How does Google ADK connect to MCP servers?
Import the MCP toolset class and pass the server URL. ADK discovers and registers all tools automatically, making them available to your agent's tool-use loop.
Can ADK agents use multiple MCP servers?
Yes. Declare multiple MCP tool providers in your agent configuration. ADK merges all tool schemas and the agent can call tools from any server in a single turn.
Which Gemini models work best with MCP tools?
Gemini 2.0 Flash and Pro models both support function calling required for MCP tools. Flash is recommended for latency-sensitive use cases, Pro for complex reasoning.
McpToolset not found
Update: pip install --upgrade google-adk
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