Brain Server - MCP Knowledge Embedding Service
About
# Brain Server - MCP Knowledge Embedding Service A powerful MCP (Model Context Protocol) server for managing knowledge embeddings and vector search. ## Features - **Vector Embeddings**: Generate high-quality embeddings for knowledge content - **Semantic Search**: Find knowledge based on meaning, not just keywords -…
Explore
- Vector Embeddings: Generate high-quality embeddings for knowledge content
- Semantic Search: Find knowledge based on meaning, not just keywords
- MCP Compliance: Follows Model Context Protocol for AI integration
- Brain Management: Organize knowledge into domain-specific brains
- Context-Aware Retrieval: Includes surrounding context for better understanding
- Progress Tracking: Real-time monitoring of long-running operations
npm install
cp .env.example .env
PORT=3000
MONGODB_URI=mongodb://localhost:27017
EMBEDDING_MODEL=Xenova/all-MiniLM-L6-v2
MAX_CHUNK_SIZE=1024
The server exposes the following MCP tools:
- addKnowledge: Add new knowledge to the vector database
- searchSimilar: Find semantically similar content
- updateKnowledge: Update existing knowledge entries
- deleteKnowledge: Remove knowledge entries
- batchAddKnowledge: Add multiple knowledge entries in a batch
- getEmbedding: Generate embeddings for text content
addKnowledge
Add new knowledge to the vector database
searchSimilar
Find semantically similar content
updateKnowledge
Update existing knowledge entries
deleteKnowledge
Remove knowledge entries
batchAddKnowledge
Add multiple knowledge entries in a batch
getEmbedding
Generate embeddings for text content
The server exposes the following MCP tools:
- addKnowledge: Add new knowledge to the vector database
- searchSimilar: Find semantically similar content
- updateKnowledge: Update existing knowledge entries
- deleteKnowledge: Remove knowledge entries
- batchAddKnowledge: Add multiple knowledge entries in a batch
- getEmbedding: Generate embeddings for text content
A powerful MCP (Model Context Protocol) server for managing knowledge embeddings and vector search.
Features
- Vector Embeddings: Generate high-quality embeddings for knowledge content
- Semantic Search: Find knowledge based on meaning, not just keywords
- MCP Compliance: Follows Model Context Protocol for AI integration
- Brain Management: Organize knowledge into domain-specific brains
- Context-Aware Retrieval: Includes surrounding context for better understanding
- Progress Tracking: Real-time monitoring of long-running operations
Installation
```bash
Sign in to leave a review
Use Google, GitHub, or an email account so ratings stay tied to real people.
No reviews posted yet.
