Wave OS MCP Server

Three layers, one MCP server. Base44 handles your data, Theta handles your compute, and Wave OS handles your intelligence — all accessible from any AI coding assistant via the Model Context Protocol.

Built by xBuildy for the Base44 Dev Build-Off — July 2026.

Architecture

Cursor / Claude Code / Any MCP Client
        │
        ▼
┌─────────────────────────────────────────┐
│         Wave OS MCP Server              │
│                                         │
│  Layer 1: Base44 Backend (Data)        │
│  ├─ Entity CRUD (create, read, update)  │
│  ├─ Aggregation pipelines               │
│  ├─ Backend function calls              │
│  └─ File uploads                        │
│                                         │
│  Layer 2: Theta Compute (GPU + Chain)   │
│  ├─ AI model inference                  │
│  ├─ GPU instance management             │
│  ├─ Smart contract deployment           │
│  ├─ Contract reads (view functions)     │
│  ├─ Wallet balance queries              │
│  └─ Transaction lookups                 │
│                                         │
│  Layer 3: Wave OS Intelligence          │
│  ├─ Morning briefing (Chief of Staff)   │
│  ├─ Triage scanning                     │
│  ├─ Follow-up tracking                  │
│  ├─ Meeting prep                        │
│  ├─ Memory save/recall                  │
│  ├─ Sub-agent delegation                │
│  └─ Wave Assistant chat                 │
└─────────────────────────────────────────┘
        │              │              │
        ▼              ▼              ▼
   Base44 API    Theta EdgeCloud   Theta RPC
   (database,    (GPU compute,     (blockchain,
    functions,    AI inference)     smart contracts)
    auth, files)

Quick Start

1. Install dependencies

cd wave-mcp-server
npm install

2. Set environment variables

export BASE44_APP_ID="your_app_id"
export BASE44_API_KEY="your_api_key"
export THETA_API_KEY="your_theta_api_key"
export THETA_PROJECT_ID="your_theta_project_id"

3. Build

npm run build

4. Configure in Cursor

Add to ~/.cursor/mcp.json:

{
  "mcpServers": {
    "wave-os": {
      "command": "node",
      "args": ["/path/to/wave-mcp-server/dist/index.js"],
      "env": {
        "BASE44_APP_ID": "your_app_id",
        "BASE44_API_KEY": "your_api_key",
        "THETA_API_KEY": "your_theta_api_key",
        "THETA_PROJECT_ID": "your_theta_project_id"
      }
    }
  }
}

5. Run in Cursor

Restart Cursor, open a new chat, and the Wave OS tools will be available to the AI assistant.

Available Tools (21 total)

Layer 1: Base44 Backend (9 tools)

Tool	Description
list_entities	List all entity schemas
create_entity	Create a new entity schema
read_records	Read/filter/sort/paginate records
create_records	Create one or more records
update_records	Update records matching a filter
delete_records	Delete records matching a filter
aggregate_records	Run MongoDB aggregation pipelines
call_function	Call any deployed backend function
upload_file	Upload a file to public storage

Layer 2: Theta Compute (8 tools)

Tool	Description
theta_list_models	List available AI models
theta_run_inference	Run AI inference on Theta EdgeCloud
theta_check_gpu_status	Check running GPU instances
theta_estimate_cost	Estimate TFUEL cost for a job
theta_deploy_contract	Deploy a smart contract to Theta mainnet
theta_read_contract	Call a view function on a deployed contract
theta_get_balance	Get wallet TFUEL and WAVE token balances
theta_get_transaction	Fetch transaction details by hash

Layer 3: Wave OS Intelligence (8 tools)

Tool	Description
wave_morning_briefing	Get aggregated daily briefing
wave_triage	Scan for urgent items across all entities
wave_follow_up_scan	Check overdue/due/upcoming follow-ups
wave_meeting_prep	Get preparation context for a meeting
wave_save_memory	Save a memory (auto-categorized)
wave_recall_memory	Search memories by keyword/category
wave_delegate_subagent	Delegate a task to a sub-agent
wave_chat	Chat with Wave OS AI Assistant

Demo Flows

Flow 1: Full-Stack Build

You: "Create a Customer entity on my Base44 app"
AI: [calls create_entity] → Entity created with fields: name, email, status

You: "Add 5 sample customers"
AI: [calls create_records] → 5 records inserted

You: "Run sentiment analysis on these customers"
AI: [calls call_function to deploy sentiment function]
    [calls theta_run_inference to analyze each customer]
    [calls update_records to store sentiment scores]

You: "Show me the results"
AI: [calls read_records with fields projection] → Returns enriched data

Flow 2: Intelligence Layer

You: "What needs my attention today?"
AI: [calls wave_triage] → Returns: 2 calendar conflicts, 1 credit warning, 3 overdue tasks

You: "Save a note that Customer X is VIP"
AI: [calls wave_save_memory] → Memory saved with category "contact"

You: "Delegate a follow-up task to a sub-agent for Customer X"
AI: [calls wave_delegate_subagent] → Sub-agent spawned, task assigned

Flow 3: Blockchain + Data

You: "Check the treasury wallet balance"
AI: [calls theta_get_balance] → Returns TFUEL and WAVE balances

You: "How many token holders do we have?"
AI: [calls read_records on TokenHolder entity] → Returns holder list

You: "Deploy a new TNT20 token contract"
AI: [calls theta_deploy_contract] → Contract deployed to mainnet
    [calls create_records to log the deployment]

Competition Pitch

Three layers, one MCP server. Base44 handles your data, Theta handles your compute, and Wave OS agent orchestration handles your intelligence. Your AI coding assistant can create an entity, deploy a function, provision a GPU, run inference, delegate a task to a sub-agent, get a morning briefing, and save a memory — all from a single prompt in Cursor.

License

MIT © xBuildy