search_knowledge_baseLABSCO SUMMARY
The knowledge base is yours; the server is just the door into it.
Everything runs in your AWS account with no vector database sitting idle between queries — the README puts the running cost at $7-10 a month for a thousand documents on Textract and Haiku. The consequence is that the deployment comes first: without a stack there is nothing for the MCP server to query. Timestamped media results are the part that is hard to get elsewhere — a transcript segment that links back to the second it was said.
What it is
The MCP front end for RAGStack-Lambda, a scale-to-zero document and media pipeline on AWS. Uploaded files are OCR'd or transcribed, embedded and stored in a Bedrock knowledge base; this server lets an assistant search that knowledge base directly.
What you get
- Search over everything the pipeline has indexed, from a conversation rather than the dashboard or a GraphQL call
- Documents in HTML, TXT, CSV, JSON, XML, EML, EPUB, DOCX and XLSX, extracted directly; PDF, JPG, PNG, TIFF, GIF, BMP, WebP and AVIF through OCR
- Video and audio in MP4, WebM, MP3, WAV, M4A, OGG and FLAC, transcribed by AWS Transcribe and cut into 30-second segments so results carry timestamps
- Answers come back with source attribution, and media sources carry the position to play from
- Metadata filtering narrows results, with a relevancy boost that prioritises matches from those filters
Requirements
A deployed RAGStack stack in your own AWS account — one-click from CloudFormation, or python publish.py from source with Python 3.13 and Node.js 24. The MCP piece is the ragstack-mcp package, run with uvx, and needs two values from your deployment: RAGSTACK_GRAPHQL_ENDPOINT and RAGSTACK_API_KEY, the latter from Dashboard then Settings.
Setup effort
One command plus a key — pip install ragstack-mcp, then supply credentials
USE CASES
01
Asking a question across a pile of documents
Retrieval with source attribution, so the answer names where it came from.
02
Finding a moment in a recording
Speech is transcribed into 30-second segments, so a search result points at a position in the file.
03
Narrowing a search that returns too much
Metadata filters cut the candidate set and push those matches up the ranking.
6 tools
Listed in project docs. Your client may show a different list.
chat_with_knowledge_basestart_scrape_jobget_scrape_job_statuslist_scrape_jobsupload_document_urlBEFORE YOU INSTALL
What to know first
Tools you get
6 tools named in the project's documentation.
Requires
- Python 3.13+
- RAGSTACK_API_KEY in your environment
- RAGSTACK_GRAPHQL_ENDPOINT in your environment
Where it runs
Both documented clients run the server on your machine.
INSTALLATION
Connect your client
Server setup
TERMINAL
pip install ragstack-mcpMCP Clients
Claude Desktop, Amazon Q
JSON
{
"mcpServers": {
"ragstack-kb": {
"command": "uvx",
"args": [
"ragstack-mcp"
],
"env": {
"RAGSTACK_GRAPHQL_ENDPOINT": "<YOUR_RAGSTACK_GRAPHQL_ENDPOINT>",
"RAGSTACK_API_KEY": "<YOUR_RAGSTACK_API_KEY>"
}
}
}
}
