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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)

Related MCP server: Tyra Advanced Memory MCP Server

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

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