D365FO MCP Server
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@D365FO MCP Servershow me the first 5 customers"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
D365FO MCP 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
Related MCP server: D365 Finance & Operations MCP Server
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 sectionManual 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.pyThis script will:
Connect to your D365 environment using credentials from
.envDownload 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 logsClaude 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 relationships2. 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 guidance3. 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, examplesDevelopment
# Install development dependencies
uv sync --extra dev
# Run tests
pytest
# Format code
ruff format .
# Type check
mypy src/d365fo_mcpArchitecture
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.
This server cannot be installed
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Latest Blog Posts
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/dougdaley/d365fo-mcp'
If you have feedback or need assistance with the MCP directory API, please join our Discord server