D365FO Server

A production-ready Model Context Protocol (MCP) server for Microsoft Dynamics 365 Finance & Operations with dependency injection architecture, automatic token refresh, and intelligent entity instructions that learn and improve over time.

Features

🔍 Smart Entity Discovery - Comprehensive D365 entity and field discovery with relevance-scored search and pagination
🔗 Entity Relationships - Navigation properties and relationship query guidance
📋 Advanced Enum Support - Complete enum definitions with OData syntax generation and entity-specific enum field discovery
⚡ Robust Operations - OData CRUD operations with automatic token refresh and intelligent company filtering
🧠 Learning System - Save, merge, and reuse successful entity usage patterns with analytics
🏗️ Enterprise Architecture - Dependency injection, repository pattern, factory pattern for extensibility
🔐 Production Authentication - Automatic OAuth token refresh on expiration
📱 Local First - SQLite storage, optimized queries, background sync capabilities

Quick Start

# Create virtual environment with uv
uv venv

# Activate virtual environment
source .venv/bin/activate  # On macOS/Linux
# or
.venv\Scripts\activate     # On Windows

# Install dependencies
uv sync

# Configure D365 connection
cp .env.example .env
# Edit .env with your D365 credentials

# Run the MCP server
python -m d365fo_mcp.main

# Or install in Claude Desktop - see Configuration section

Manual Metadata Sync

The server automatically syncs D365 metadata in the background, but you can also manually populate the metadata cache if needed:

# Run manual metadata sync (one-time setup or refresh)
python scripts/manual_sync.py

This script will:

• Connect to your D365 environment using credentials from .env
• Download complete metadata (typically 40-50MB)
• Process 4,000+ entities and their properties
• Populate the local SQLite database for fast searches
• Complete in 15-30 seconds depending on your connection

When to use manual sync:

• Initial setup to verify credentials and connectivity
• If background sync isn't working properly
• To refresh metadata after major D365 customizations
• For troubleshooting connectivity issues

Configuration

Environment Variables

Create a .env file with your D365 credentials:

# D365 Authentication (Required)
AZURE_CLIENT_ID=<service-principal-client-id>
AZURE_CLIENT_SECRET=<service-principal-secret>
AZURE_TENANT_ID=<azure-tenant-id>
D365_BASE_URL=<d365-full-url>

# Optional Configuration
DATAAREAID=usmf                      # Default company ID
DATABASE_PATH=./data/d365fo-mcp.db   # SQLite database location (auto-created)
METADATA_CACHE_HOURS=24              # Metadata cache duration
LOG_LEVEL=info                       # Logging verbosity
DEBUG=false                          # Enable debug mode
SQLITE_ECHO=false                    # Echo SQLite queries to logs

Claude Desktop Integration

Add to your Claude Desktop configuration (~/Library/Application Support/Code/User/mcp.json):

{
  "mcpServers": {
    "D365FO MCP Server": {
      "command": "/path/to/.venv/bin/python",
      "args": ["-m", "d365fo_mcp.main"],
      "cwd": "/path/to/d365fo-mcp",
      "env": {
        "PYTHONPATH": "/path/to/d365fo-mcp",
        "AZURE_CLIENT_ID": "your-client-id",
        "AZURE_CLIENT_SECRET": "your-client-secret", 
        "AZURE_TENANT_ID": "your-tenant-id",
        "D365_BASE_URL": "https://your-env.sandbox.operations.dynamics.com",
        "DATAAREAID": "usmf",
        "DATABASE_PATH": "/absolute/path/to/d365fo-mcp/data/d365fo-mcp.db"
      }
    }
  }
}

⚠️ Important: Use absolute paths for DATABASE_PATH in Claude Desktop configuration.

Usage Workflows

1. Entity Discovery with Relevance Search

// Search for entities with intelligent relevance scoring
search_entities("Customer", limit=10, skip=0)
// Returns: exact matches first, then partial matches with relevance scores

// Get comprehensive entity metadata (REQUIRED before operations)
get_entity_metadata("CustomersV3") 
// Returns: field definitions, data types, required fields, enum fields

// Get specific field definitions 
get_entity_fields("CustomersV3")
// Returns: detailed field info with constraints and relationships

2. Advanced Enum Operations

// Search for enums with pagination
search_enums("Status", limit=20, skip=0)

// Get enum with OData syntax generation
get_enum_metadata("CustVendorBlocked")
// Returns: enum values + exact OData syntax for filtering

// Find all enum fields in a specific entity
get_entity_enum_fields("CustomersV3")
// Returns: field names, enum types, and usage guidance

3. Robust Data Operations with Auto-Retry

// Query with automatic token refresh on 401 errors
get_odata_entity("CustomersV3", 
    filter_query="CustomerGroupId eq 'RETAIL'",
    select_fields="CustomerAccount,CustomerName,CustomerGroupId",
    top=50,
    dataareaid="usmf"
)

// Use wildcard searches (D365 OData syntax)
get_odata_entity("LedgerJournalTransBiEntities",
    filter_query="LedgerDimensionValuesJson eq '*50111*' and TransDate ge 2025-07-01T00:00:00Z",
    top=100
)

// Create with proper field validation
create_odata_entity("CustomersV3", 
    data={
        "CustomerAccount": "CUST001",
        "CustomerName": "Test Customer",
        "CustomerGroupId": "RETAIL"
    },
    dataareaid="usmf"
)

4. Learning System with Merge Intelligence

// Save successful patterns that merge with existing instructions
save_entity_instruction(
    entity_name="TrialBalanceFiscalYearSnapshots",
    operation_type="read", 
    instruction={
        "title": "Trial Balance Account Reconciliation",
        "description": "Query pattern for account reconciliations using DimensionValue1 for main accounts...",
        "example_query": "DimensionValue1 eq '50111' and PeriodEndDate ge 2025-07-01T00:00:00Z",
        "tags": ["trial-balance", "reconciliation", "financial-reporting"]
    },
    update_mode="merge"  // Intelligently combines with existing instructions
)

// Get learned patterns with success analytics
get_entity_instructions("TrialBalanceFiscalYearSnapshots", "read")
// Returns: instructions, success rates, usage patterns, examples

Development

# Install development dependencies
uv sync --extra dev

# Run tests
pytest

# Format code
ruff format .

# Type check
mypy src/d365fo_mcp

Architecture

Core Framework:

• FastMCP 2.0 - Production MCP server framework with tool registry
• Dependency Injection - Repository, Service, and Factory patterns for extensibility
• SQLite - High-performance local storage with WAL mode and optimized indexes

Authentication & Communication:

• Azure Identity - Service principal authentication with automatic token refresh
• HTTPX - Async HTTP client with retry logic and timeout handling
• Automatic Token Refresh - Handles OAuth token expiration transparently

Data Layer:

• Repository Pattern - Pluggable storage backends (SQLite, future Supabase)
• Optimized Queries - Relevance scoring, pagination, full-text search
• Background Sync - Metadata caching with intelligent refresh strategies

Business Logic:

• Service Layer - Clean separation between tools and data access
• Instruction Learning - Pattern recognition and success analytics
• Company Context - Intelligent multi-company operation handling

See APPROACH.md for detailed architectural decisions and technical approach.

License

MIT License - see LICENSE file for details.