lucamilletti99/dataverse_mcp_server

🔌 Dataverse MCP Server

A Model Context Protocol (MCP) server for Microsoft Dataverse, enabling AI assistants like Claude to interact with Dataverse data through natural language.

Built on: Model Context Protocol | Microsoft Dataverse MCP Spec

What is This?

This MCP server exposes Microsoft Dataverse tables and records through the Model Context Protocol, allowing AI assistants to:

• 📊 List and explore Dataverse tables (entities)
• 🔍 Query records with OData filters and sorting
• ✏️ Create new records in any table
• 🔄 Update existing records by GUID
• 🤖 Natural language interface via Claude Desktop, VS Code, or any MCP client

Quick Start

1. Prerequisites

• Python 3.11+
• Dataverse environment with API access
• Azure AD app registration with Dataverse permissions

2. Install

# Install dependencies
pip install fastapi uvicorn requests python-dotenv fastmcp

# Or with uv (recommended)
uv pip install fastapi uvicorn requests python-dotenv fastmcp

3. Configure

Run the setup script:

chmod +x setup_dataverse.sh
./setup_dataverse.sh

Or manually create .env.local:

DATAVERSE_HOST=https://org1bfe9c69.api.crm.dynamics.com
DATAVERSE_TENANT_ID=your-tenant-id
DATAVERSE_CLIENT_ID=your-client-id
DATAVERSE_CLIENT_SECRET=your-client-secret

4. Test Connection

python test_dataverse.py

Expected output: ✅ All tests passed! Dataverse MCP server is ready.

5. Start Server

Option A: Run Locally

./watch.sh
# Or: uvicorn server.app:combined_app --reload --port 8000

Option B: Deploy to Databricks Apps

./deploy.sh --create

📖 See for deployment guide.

Server endpoints:

• 🔧 MCP: http://localhost:8000/mcp (local) or https://your-app.databricksapps.com/apps/your-app/mcp (cloud)
• 📖 Docs: http://localhost:8000/docs
• ❤️ Health: http://localhost:8000/api/health

Documentation

Available MCP Tools

Phase 1 (Implemented)

Phase 2 (Planned)

• delete_record - Delete records
• create_table / update_table / delete_table - Table management
• list_knowledge_sources / retrieve_knowledge - Copilot Studio integration
• list_prompts / execute_prompt - Custom prompts

Usage Examples

Claude Desktop

Configure ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "dataverse": {
      "url": "http://localhost:8000/mcp"
    }
  }
}

Then in Claude:

You: "List all custom tables in my Dataverse environment"
Claude: [Calls list_tables with custom_only=True]

You: "Show me accounts with revenue over $1M"
Claude: [Calls read_query with filter]

You: "Create a new account called Fabrikam with $2M revenue"
Claude: [Calls create_record]

cURL

# List tables
curl -X POST http://localhost:8000/mcp/call-tool \
  -H "Content-Type: application/json" \
  -d '{"name": "list_tables", "arguments": {"top": 10}}'

# Query accounts
curl -X POST http://localhost:8000/mcp/call-tool \
  -H "Content-Type: application/json" \
  -d '{
    "name": "read_query",
    "arguments": {
      "table_name": "account",
      "select": ["name", "revenue"],
      "filter_query": "revenue gt 1000000",
      "top": 10
    }
  }'

Python

import requests

response = requests.post('http://localhost:8000/mcp/call-tool', json={
    "name": "describe_table",
    "arguments": {"table_name": "account"}
})

print(response.json())

Project Structure

dataverse_mcp_server/
├── server/
│   ├── app.py                      # FastAPI + MCP server
│   ├── dataverse_tools.py          # MCP tool implementations
│   ├── dataverse/
│   │   ├── auth.py                 # OAuth authentication
│   │   ├── client.py               # Dataverse Web API client
│   │   └── __init__.py
│   └── routers/                    # FastAPI REST endpoints
│       ├── health.py               # Health check
│       ├── mcp_info.py             # MCP metadata
│       └── user.py                 # User info
├── client/                         # React frontend (future)
├── test_dataverse.py              # Connection tests
├── setup_dataverse.sh             # Interactive setup
├── watch.sh                       # Dev server with hot reload
├── config.yaml                    # MCP server config
├── env.example                    # Environment template
├── DATAVERSE_QUICKSTART.md        # Quick start guide
├── DATAVERSE_SETUP.md             # Detailed setup
└── README_DATAVERSE.md            # Full reference

Architecture

Authentication: Service Principal (OAuth 2.0 client credentials flow)
API: Dataverse Web API v9.2
Protocol: Model Context Protocol (MCP)
Framework: FastAPI + FastMCP

MCP Clients (Claude, VS Code) 
    ↓ HTTP/MCP
Dataverse MCP Server (FastAPI)
    ↓ HTTPS/OAuth
Microsoft Dataverse

Troubleshooting

See for detailed troubleshooting.

Development

# Run tests
python test_dataverse.py

# Start dev server
./watch.sh

# Check lints
ruff check server/

# Format code
ruff format server/

Reference Links

• Dataverse Web API Documentation
• Model Context Protocol
• Microsoft Dataverse MCP
• OAuth with Dataverse

License

See

Security

Report vulnerabilities via

Questions? See or

Built with ❤️ for the Dataverse and MCP communities