Amazon Bedrock KB MCP. Grounded Answers from Your Private Data Sources
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Amazon Bedrock KB connects your AI agent directly to AWS Bedrock Knowledge Bases, allowing semantic search and managed Retrieval-Augmented Generation (RAG).
It lets you query massive corporate datasets—like S3 buckets or internal documents—by executing vector searches without building custom data pipelines.
You get grounded LLM responses by letting your agent access proprietary knowledge exactly where it lives in AWS.
What your AI agents can do
List knowledge bases
Provides a comprehensive list of all available Amazon Bedrock knowledge bases within your account region.
Get knowledge base
Fetches the detailed configuration parameters for a specific AWS Bedrock knowledge base instance.
Retrieve
Executes a pure vector query to pull raw text chunks from the index without generating an answer.
You check which Amazon Bedrock Knowledge Bases are configured and active in your region.
The agent fetches the explicit configuration parameters for a single, identified Knowledge Base instance.
You list and inspect which external storage buckets are actively feeding data into your knowledge base.
The system tracks the real-time operational status of document ingestion jobs, confirming chunking pipelines completed without errors.
Your agent runs a precise query against the vector index to pull back the top text chunks and their source URLs.
The MCP combines retrieval and generation, producing an LLM answer that is explicitly cited using material from your internal documents.
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Supported MCP Clients
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Amazon Bedrock KB: 6 Tools
These tools let you manage the lifecycle of your knowledge base, from listing available resources to running highly accurate, grounded retrieval queries.
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Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using Amazon Bedrock KB on Vinkius019d755alist knowledge bases
Provides a comprehensive list of all available Amazon Bedrock knowledge bases within your account region.
019d755aget knowledge base
Fetches the detailed configuration parameters for a specific AWS Bedrock knowledge base instance.
019d755aretrieve
Executes a pure vector query to pull raw text chunks from the index without generating an answer.
019d755aretrieve and generate
Generates a complete, grounded LLM response by first retrieving relevant context and then synthesizing an answer using it.
019d755alist data sources
Retrieves a list of all external storage buckets currently bound to an Amazon Bedrock Knowledge Base.
019d755alist ingestion jobs
Shows the status and history of document syncing operations running through AWS Bedrock's chunking pipelines.
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Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Amazon Bedrock. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 6 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Sourcing Answers from Internal Documentation
Before this, getting an AI to reference internal documents meant manual work. A developer had to connect various systems; they'd write custom scripts to pull data from S3, chunk it into embeddings, and manually manage the sync process—a brittle workflow that constantly required maintenance.
Now, connecting your agent is a single step via this MCP. You simply point it at your AWS resources. The system handles the complexity: indexing the source files, managing the chunks, and making the data available for queries when you need them.
How to Access Knowledge with `retrieve_and_generate`
The manual steps of querying raw vector indices and then feeding those chunks into a separate generation model are gone. You don't have to manage two distinct API calls or piece together the context yourself.
You just run `retrieve_and_generate`. The tool handles both retrieval and synthesis in one explicit call, giving you an immediate, grounded answer that cites its sources.
What you can do with this MCP connector
This MCP connects your AI client to Amazon Bedrock's full suite of knowledge management tools. It lets your agent perform complex information retrieval directly from private, internal document stores inside AWS. Instead of relying on generic internet data, your agent queries vector indices built from your own documents—think HR manuals or engineering specs.
The system manages the entire process: it indexes your source files, chunks them into manageable pieces, and performs semantic searches when prompted. You don't need to build custom ingestion pipelines; you just connect your credentials and start querying. If you’re building an agent that must reference specific corporate policies, this MCP gives it access to massive datasets precisely where they reside in AWS.
Vinkius hosts this capability so you can give your agent reliable, grounded context from day one.
019d755a-0f40-7136-82d4-720564a0e6e1 
How Amazon Bedrock KB MCP Works
- 1 First, subscribe to the MCP and provide your AWS IAM Role or User Access Credentials.
- 2 Second, configure your agent client to augment its context using these credentials; this connects it to Bedrock's services.
- 3 Third, invoke a retrieval tool. The process executes the semantic search against your attached data sources and returns a grounded answer.
The bottom line is that you connect your existing AWS resources—like S3 buckets—and start querying them immediately through your agent client.
Who Is Amazon Bedrock KB MCP For?
This MCP is essential for any developer or architect whose job involves making sure an AI model uses current, private corporate data. It solves the problem of needing to build and maintain complex vector infrastructure from scratch.
You use this MCP to rapidly prototype RAG workflows; you don't have to worry about hosting databases or managing chunking sync logic.
You audit the system, using tools like list_data_sources and list_ingestion_jobs, to verify that all connected buckets are mapping data correctly into the index.
You prototype context-grounded queries instantly; you can trace the accuracy of an answer back to the exact source document and chunk.
What Changes When You Connect
- You eliminate custom vector pipeline development. By using this MCP, your agent queries massive corporate datasets directly where they sit in AWS.
- The
list_ingestion_jobstool lets you track sync status in real time; you know immediately if new documents are being chunked and mapped correctly. - Instead of simple keyword searches, the
retrieve_and_generatefunction performs semantic retrieval, understanding context to give accurate answers. - You can audit your connections using
list_data_sources; this confirms exactly which S3 buckets are feeding knowledge into the system. -
get_knowledge_basegives you explicit control over the KB's configuration; you see the assigned embedding models and boundaries. - If you only need the source material, use
retrieve. This function pulls the top-K text chunks and their original document URLs without attempting to write an answer.
Real-World Use Cases
Onboarding HR Policies
An HR specialist asks their agent: 'What's the policy for remote work equipment?' The agent uses list_knowledge_bases to select the Policy KB, then runs a query via retrieve_and_generate. It returns an answer citing the exact section in the company handbook and listing required forms.
Debugging Data Sync
An operations engineer notices stale data. They use list_ingestion_jobs to check the status of the Documentation bucket's sync job, confirming if it completed successfully at 08:30 and detailing how many new documents were processed.
Retrieving Source Code Context
A developer needs to know the specific AWS service parameters for a legacy system. They run list_data_sources to confirm the correct S3 bucket, then use retrieve to pull the precise configuration text chunks needed for coding.
Validating System Scope
A cloud architect needs to know what knowledge bases exist. They call list_knowledge_bases, which instantly shows all active KBs and their associated embedding models, preventing scope creep.
The Tradeoffs
Using simple text search
A user tries to ask the agent a question and simply expects a keyword match. The answer is vague because it only finds surface-level terms, not contextual meaning.
→
To get contextually accurate answers, use retrieve_and_generate. This tool ensures the LLM first pulls relevant document chunks (retrieval) before writing an answer (generation).
Assuming data is ready
The agent attempts to query a Knowledge Base that hasn't finished syncing its source files, resulting in incomplete or outdated information.
→
Always check the status first. Run list_ingestion_jobs to confirm that the chunking pipeline completed successfully before relying on the data for any critical query.
Querying without source knowledge
A user asks a question but fails to specify which department or dataset it relates to. The agent either refuses or returns generic results.
→
Start by using list_knowledge_bases to narrow the scope. Select the correct KB ID; this grounds your query to the right corpus.
When It Fits, When It Doesn't
Use this MCP if the core requirement is grounding LLM output in proprietary, structured corporate documents. This is for enterprise RAG. Don't use it if you just need a general web search or simple API calls; those cases require different tools entirely. If your data lives in an S3 bucket or another AWS resource, and you need semantic understanding of that data, this MCP is required. The key distinction is between retrieve (which only gives text chunks) and retrieve_and_generate (which gives a usable answer). You must use the latter for user-facing applications.
Common Questions About Amazon Bedrock KB MCP
How do I know what knowledge bases are available? (using list_knowledge_bases) +
You run list_knowledge_bases. This command immediately lists all the Amazon Bedrock KBs configured in your account, so you can pick the correct one for your query.
Do I need to worry about data sync status? (using list_ingestion_jobs) +
Yes. The list_ingestion_jobs tool lets you check the real-time status of all chunking pipelines, ensuring your source documents are fully mapped before querying.
What's the difference between retrieve and retrieve_and_generate? (using both) +
retrieve only pulls raw text snippets from the vector index; it doesn't write an answer. Use retrieve_and_generate when you need a complete, synthesized response grounded in those documents.
How do I see what data sources are attached to my KB? (using list_data_sources) +
Use the list_data_sources tool. It provides an explicit list of all external storage buckets, confirming exactly where your knowledge base pulls its information from.
When I run a query using the `retrieve` tool, does it enforce specific AWS security policies or IAM roles? +
Yes, the operation runs strictly under your provided AWS credentials. This means the retrieval is limited to only those data sources and knowledge bases your role has explicit read permissions for.
Before running complex queries, how can I use `get_knowledge_base` to confirm the setup parameters of my Bedrock Knowledge Base? +
This tool returns the KB's core configuration details. You can verify things like the assigned embedding model and regional settings before attempting any retrieval.
If I use `list_data_sources`, can I confirm if the underlying document format is compatible with chunking? +
The tool provides metadata about all attached sources. This helps you check if the source bucket contains formats that Bedrock's vector ingestion pipeline supports.
If my queries fail, how can I use `list_knowledge_bases` to identify all available KBs and catch potential ID errors? +
Running this tool gives you a definitive list of every KB in your region. Comparing the listed IDs against your query ensures you're targeting the correct resource.
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.
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