Server Claude

MCP Memory Server for Claude Code — persistent context across sessions using semantic search (RAG).

Problem

Claude Code compacts conversation history when it exceeds the context window (~200k tokens). This causes loss of operational context between sessions. A database alone doesn't fix this — the solution is selective context retrieval (RAG), not full history loading.

Solution

A local MCP server that:

1. Saves key fragments from each session with embeddings
2. On new sessions, retrieves only the top-K semantically relevant fragments (~5-10k tokens)
3. Provides structured project context (decisions, status, todos) on demand

Architecture

Claude Code CLI
      │ MCP Protocol (JSON-RPC 2.0 / stdio)
      ▼
MCP Memory Server (FastMCP)
  ├── Tools: remember, recall, get_project_context, list_sessions
  ├── ChromaDB  ← vector store (semantic search)
  └── SQLite    ← session metadata

Quick Start

git clone <repo>
cd mcp-server-claude
pip install -r requirements.txt
cp .env.example .env
# edit .env — set MEMORY_DB_PATH to a directory outside the repo

# Register with Claude Code:
# Add the contents of mcp_config.example.json to ~/.claude/settings.json

Environment

Designed for Zurich Insurance GenAI Platform (LiteLLM proxy UAT, WSL2 Ubuntu 24).

• CLAUDE_CODE_DISABLE_PROMPT_CACHING=1 — proxy doesn't support caching; this server compensates
• All embeddings run locally (sentence-transformers) — no external calls

Documentation

• Architecture & Analysis
• Managed Projects