A read-only MCP server for searching and browsing a local AI knowledge base powered by hybrid vector + full-text retrieval. It exposes four tools:
search(query, limit, hybrid) — Perform semantic or hybrid (vector + FTS → RRF fusion → optional cross-encoder rerank) searches. Supports configurable result limits and hybrid mode toggle. Results cite source_id/source_url, never raw filepaths.
discover(mode, days, limit) — Browse content without a specific query using four non-ranking modes:
digest — Recent items over a configurable time window (default: last 7 days)
random — Random content exploration
concepts — Top mentioned terms via heuristic extraction
channels — Content grouped by source channel
get_context(query, include_recent) — Retrieve comprehensive context for a query, optionally appending a recent digest for broader situational awareness.
get_status() — Check knowledge base statistics (document counts, tracked channels) and Ollama/embedding service health.
Note: Mutation tools (
add_channel,sync_now) are not available on the public profile — this server is strictly read-only unless the private env flagAI_KB_MCP_PRIVATE=1is set locally.
Allows ingestion of YouTube video transcripts from configured channels, enabling semantic and hybrid search over their content.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@AI Knowledge Base MCP Server search for 'hybrid retrieval' concepts"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Keep coding agents current with local hybrid RAG + MCP.
Public demo uses synthetic fixtures; the architecture is the product.
AI techniques move weekly. Coding agents that only “know” last quarter’s defaults fall behind. Teams need a local, citable knowledge path — retrieve what matters, cite sources, and expose tools agents can call — without shipping a private corpus to the public internet.
flowchart LR
F[Fixtures / sources] --> I[Ingest + embed]
I --> D[(LanceDB)]
Q[Query] --> S[Hybrid search]
D --> S
S --> M[CLI / MCP tools]
M --> A[Coding agents]Ingest documents (committed fixtures for the public demo).
Embed locally (Ollama nomic-embed-text).
Retrieve with vector + keyword fusion, optional cross-encoder.
Serve results via CLI and read-only MCP tools (search, discover, get_context, get_status).
Hybrid fusion before cross-encoder — the retrieval spine stays useful if the reranker degrades or is disabled.
Public fixtures / private corpus split — strangers get a working demo; personal tip libraries stay off this repo.
MCP read-only by default — mutations stay behind an explicit private profile flag.
uv sync
ollama pull nomic-embed-text
uv run python -m src.ingest --fixtures
uv run python -m src.search "reciprocal rank fusion RRF" --hybrid --db data/lancedb
uv run python -m src.evalMCP wiring, discovery commands, and optional BYO YouTube overlay: GETTING_STARTED.md.
Component | Tool |
Vector + FTS | LanceDB |
Embeddings | Ollama · |
Rerank (optional) | MiniLM cross-encoder (degrades to fusion) |
Agent surface | MCP (public profile = read-only) |
docs/PORTFOLIO_VISION.md — packaging intent
docs/ARCHITECTURE.md — contracts / how
GETTING_STARTED.md — operator path
FAQ.md — Technical FAQ
docs/2026-07-12_ce_keep_note.md — cross-encoder keep note
LICENSE — MIT
Building agent knowledge systems? Reach me on LinkedIn.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Search your knowledge bases from any AI assistant using hybrid RAG.
Agentic search over your Dewey document collections from any MCP-compatible client.
Securely search and manage workspace context files for AI agents and teams.
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curl -X GET 'https://glama.ai/api/mcp/v1/servers/Alpha-W0lf/ai-knowledge-base-public'
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