Oracle MCP
Click on "Deploy 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., "@Oracle MCPhow should I handle merge conflicts in git?"
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.
Oracle MCP - AI Knowledge Management Server
"The Oracle Keeps the Human Human" - queryable via MCP
A Model Context Protocol (MCP) server for semantic search and knowledge management. Store principles, patterns, and learnings with hybrid keyword + vector search.
Installation
As Claude Code Plugin
# Via Claude Code CLI
claude plugin add laris-co/oracle-mcpManual Setup
# Clone and install
git clone https://github.com/laris-co/oracle-mcp
cd oracle-mcp
npm install
# Run MCP server
npm run devRelated MCP server: MCP Power - Knowledge Search Server
Configuration
Add to ~/.claude/mcp.json:
{
"mcpServers": {
"oracle-mcp": {
"command": "npm",
"args": ["run", "dev"],
"cwd": "/path/to/oracle-mcp",
"env": {
"ORACLE_DATA_DIR": "/path/to/data"
}
}
}
}Environment Variables
Variable | Default | Description |
|
| Data directory for SQLite + ChromaDB |
MCP Tools
oracle_search
Hybrid search (keywords + semantic) across knowledge base.
{
"query": "how should I handle file deletion?",
"type": "principle",
"mode": "hybrid",
"limit": 5
}mode:hybrid(default),fts(keywords only),vector(semantic only)type:principle,pattern,learning,retro,all
oracle_consult
Get guidance on a decision based on stored principles.
{
"decision": "Should I amend this commit?",
"context": "I just made a commit but forgot to add a file"
}oracle_learn
Add new patterns or learnings to knowledge base.
{
"pattern": "Always verify before destructive operations",
"source": "Retrospective 2025-01-15",
"concepts": ["safety", "verification"]
}oracle_list
Browse documents without search (pagination supported).
{
"type": "learning",
"limit": 10,
"offset": 0
}oracle_reflect
Get random wisdom for reflection.
{}Data Storage
All data stored in ORACLE_DATA_DIR (default: ~/.oracle-mcp/):
~/.oracle-mcp/
├── oracle.db # SQLite database (FTS5 + metadata)
├── chroma_data/ # ChromaDB vector embeddings
└── learnings/ # Markdown files from oracle_learnDevelopment
# Install dependencies
npm install
# Run in development mode
npm run dev
# Build TypeScript
npm run build
# Run production
npm start
# Run with pm2
pm2 start ecosystem.config.cjsArchitecture
Claude Code → MCP Server → SQLite (FTS5) + ChromaDB (vectors)
↓
oracle.db + chroma_data/Following claude-mem patterns:
Hybrid search (FTS5 keywords + vector semantic)
Graceful degradation (works without ChromaDB)
Local embeddings via ChromaDB
SQLite as source of truth
License
MIT
Created: 2025-12-29 Architecture: claude-mem inspired
This server cannot be deployed
Maintenance
Related MCP Connectors
Cloud or self-hosted knowledge for AI agents: hybrid search, reranking, GraphRAG, scoped MCP tools.
Search your knowledge bases from any AI assistant using hybrid RAG.
Memory system for AI agents with semantic search. Store and recall memories with ease.
Persistent memory for AI agents. Search and store durable facts, preferences and decisions.
Related MCP Servers
- FlicenseCqualityDmaintenanceEnables storing and retrieving information using vector embeddings with semantic search capabilities. Integrates with the AI Embeddings API to automatically generate embeddings for content and perform similarity-based searches through natural language queries.2-
- AlicenseBqualityNot gradedmaintenanceEnables semantic search across multiple knowledge datasets using FAISS vector embeddings, allowing natural language queries to find relevant documents with fast retrieval.21-
- AlicenseNot gradedqualityDmaintenanceEnables AI assistants to search through structured databases and unstructured content (documents, videos, files) using natural language queries with semantic understanding.MIT

Fraim Context MCPofficial
AlicenseNot gradedqualityNot gradedmaintenanceEnables semantic search of project documentation using hybrid vector and full-text search with fast and deep query modes for immediate results or complex multi-round synthesis.MIT