flowchart LR
    subgraph BackgroundService["Background Service"]
        Server["server.py<br>MCP SSE Server<br>http://127.0.0.1:8000/sse"] --> DB[("notes.json")]
    end

    subgraph ClientApp["Client Application"]
        User["User Query"] --> Client["client.py<br>MCP Client"]
        Client -- "1. Tool Decision" --> LLM["Local Ollama LLM<br>(qwen2.5, llama3.2)"]
        Client -- "2. HTTP / SSE Calls" --> Server
    end

python server.py

==================================================
🚀 Personal Assistant Memory Server (MCP SSE Service)
📡 Listening on: http://127.0.0.1:8000/sse
🛠️  Exposed Tools: save_note, search_notes, list_notes, update_note, delete_note
💾 Database: notes.json
==================================================

python client.py

You > Remember to submit the project report by Friday. Tag it as project and report.
>> [MCP Tool Call] -> save_note
   Arguments: {"content": "Submit the project report by Friday.", "tags": ["project", "report"]}
   Result: Note saved successfully with ID: a1b2c3d4 and tags: ['project', 'report']
Assistant > Saved your reminder to submit the project report by Friday.

You > When do I need to submit the report?
>> [MCP Tool Call] -> search_notes
   Arguments: {"query": "report"}
   Result: [...]
Assistant > The project report needs to be submitted by Friday.

# Save note
python client.py "Save a note: Team standup is daily at 10 AM on Zoom. Tag: meeting, standup"

# Search notes
python client.py "When is the team standup?"

# List all notes
python client.py "Show me all my saved notes"

personal-assistant-wwllm/
├── server.py          # Persistent MCP Server (SSE / HTTP Daemon)
├── client.py          # Interactive MCP Client with Ollama LLM
├── notes.json         # Local persistent memory store
├── requirements.txt   # Dependencies
└── README.md          # Documentation