You: What's the weather in Mumbai, and save it as a note called "Mumbai weather"
→ tool get_weather {'city': 'Mumbai'}
→ tool add_note {'title': 'Mumbai weather', 'body': '29.4°C, 78% humidity, wind 14 km/h'}
╭─ Assistant ──────────────────────────────────────────────╮
│ It's 29.4°C in Mumbai with 78% humidity. Saved as note #1. │
╰──────────────────────────────────────────────────────────╯

You: Summarize project_ideas.md and tell me which one has the highest priority
→ tool read_file {'path': 'project_ideas.md'}
╭─ Assistant ──────────────────────────────────────────────╮
│ ... The Smart Study Planner is marked high priority.     │
╰──────────────────────────────────────────────────────────╯

flowchart LR
    U[User / CLI] --> G
    subgraph G[LangGraph agent]
        A[agent node<br/>OpenAI LLM] -- tool calls --> T[tools node]
        T -- results --> A
    end
    T <-- MCP over stdio or HTTP --> S
    subgraph S[FastMCP server]
        F[Files<br/>sandboxed]
        N[Notes<br/>SQLite]
        W[Weather API<br/>Open-Meteo]
        K[Wikipedia API]
        C[Calculator<br/>AST-safe]
    end

git clone https://github.com/avishiii/mcp-ai-assistant.git
cd mcp-ai-assistant
python -m venv .venv
source .venv/bin/activate          # Windows: .venv\Scripts\activate
pip install -r requirements.txt
cp .env.example .env               # then add your OPENAI_API_KEY
python main.py

python main.py --ask "What is 15% of 2480?"       # one-shot question
python main.py -v                                  # also print tool outputs

# Run the MCP server over HTTP and connect to it
python server/mcp_server.py --http 8000
python main.py --server http://127.0.0.1:8000/mcp

pip install -r requirements-dev.txt
pytest -q

mcp-ai-assistant/
├── server/
│   └── mcp_server.py        # FastMCP server: tools, resource, prompt
├── agent/
│   ├── mcp_bridge.py        # MCP client -> LangChain tools adapter
│   └── graph.py             # LangGraph state graph (agent <-> tools loop)
├── tests/
│   └── test_assistant.py    # offline test suite
├── workspace/               # sandbox folder the assistant can read/write
├── main.py                  # interactive CLI
├── .github/workflows/tests.yml
└── requirements.txt

@mcp.tool
def word_count(path: str) -> int:
    """Count the words in a workspace file."""
    return len(read_file(path).split())