isshiki-dev/kb-mcp-server
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The KB-MCP Server is a local-first knowledge base with Model Context Protocol (MCP) support, designed to provide AI with a reliable memory that operates locally and streams answers in real time.
KB-MCP Server
A local-first Knowledge Base with Model Context Protocol (MCP) support. Give your AI a reliable memory. Run it locally. Stream answers in real time.
What is This?
A Knowledge Base (KB) is a structured collection of facts, documents, and embeddings stored in machine-readable form, with interfaces to:
- Add knowledge
- Query knowledge (semantic + keyword search)
- Update/Delete knowledge
This MCP server exposes your KB to any MCP-compatible AI client (Claude, custom agents, etc.).
Why Local-First?
| Benefit | Description |
|---|---|
| Privacy | No cloud leaks — your data stays on your machine |
| Zero latency | No network round-trips |
| Offline support | Works without internet |
| Full control | You own the data and the logic |
| No vendor lock-in | Swap components freely |
Quick Start
Installation
npm install
npm run build
Run the Server
npm start
Or for development:
npm run dev
Configure with Claude Desktop
Add to your claude_desktop_config.json:
{
"mcpServers": {
"knowledge-base": {
"command": "node",
"args": ["/path/to/kb-mcp-server/dist/index.js"],
"env": {
"KB_DATA_DIR": "/path/to/your/data"
}
}
}
}
Available Tools
| Tool | Description |
|---|---|
ingest_document | Add a document with title, content, and metadata |
query_knowledge | Semantic search across all documents |
list_documents | List documents with pagination |
get_document | Get full document by ID |
update_document | Update existing document |
delete_document | Remove document from KB |
kb_stats | Get knowledge base statistics |
How It Works
1. User asks a question
↓
2. AI sends MCP query → KB-MCP Server
↓
3. KB retrieves relevant facts (semantic search)
↓
4. AI grounds the answer with real data
↓
5. Response streams to user
↓
6. (Optional) New insights stored back
Result: AI answers correctly. Knowledge compounds. No hallucinations.
Architecture
┌─────────────────┐
│ AI Client │
│ (Claude, Agent) │
└────────┬────────┘
│ MCP Protocol
↓
┌─────────────────┐
│ KB-MCP Server │ ← stdio transport
│ ┌───────────┐ │
│ │ Tools │ │ ingest | query | list | delete
│ └─────┬─────┘ │
│ ↓ │
│ ┌───────────┐ │
│ │ Engine │ │ Embeddings + Similarity Search
│ └─────┬─────┘ │
│ ↓ │
│ ┌───────────┐ │
│ │ Store │ │ JSON file (swap with Chroma/pgvector)
│ └───────────┘ │
└─────────────────┘
Configuration
| Environment Variable | Default | Description |
|---|---|---|
KB_DATA_DIR | ./.kb-data | Directory for storing knowledge base data |
Production Enhancements
For production use, consider:
- Real embeddings: Replace hash-based embeddings with OpenAI, Cohere, or local models (Ollama)
- Vector database: Swap JSON store with Chroma, Qdrant, or pgvector
- Chunking: Split large documents into chunks for better retrieval
- Hybrid search: Combine semantic + BM25 keyword search
- Access control: Add authentication for multi-user setups
License
MIT — Use freely.
Author
Matrix Agent