MCP Server for Dynamics 365 Finance & Operations

This project demonstrates how to build a custom Model Context Protocol (MCP) server that integrates Azure OpenAI with Dynamics 365 Finance & Operations (D365 F&O). The server leverages natural language input to generate valid OData queries, fetch data from D365, and process results using a Large Language Model (LLM).

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🔧 Architecture Overview

![Architecture Diagram]

Components:

• Client (User): Sends natural language queries.
• MCP Server (FastAPI): Parses the input, uses Azure OpenAI to generate an OData query.
• D365 F&O: Returns matching business data using OData.
• Azure OpenAI: Used twice — once to generate the OData query, and again to post-process the response.
• Output: Cleaned and focused data returned to the calling system.

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📦 Technology Stack

• Python 3.10+
• FastAPI
• Azure OpenAI (openai Python SDK)
• Requests (for HTTP calls)
• OAuth2 for secure access to D365 F&O

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🛠 SDKs Used

• OpenAI SDK – For communicating with Azure OpenAI deployments.
• Requests – For REST API calls to D365 and token endpoints.
• Pydantic + FastAPI – For building the RESTful MCP interface.

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🌐 Public MCP Servers (Non-Microsoft)

While this implementation is custom-built for D365 F&O, several public MCP servers exist in the ecosystem — especially for open-source or OSS-focused LLM projects:

• LangChain MCP Examples
• Haystack LLM Agent Servers
• DSPy Agent Context Servers
• AutoGen Frameworks with context protocol layers

These are usually used for document retrieval, QA systems, and autonomous agents.

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📘 Example Scenario

"Show me top 10 customers from California with credit limit over $10,000"

How It Works:

1. User sends a natural language request.
2. MCP Server uses LLM to generate this OData query:

CustomersV3?$filter=State eq 'CA' and CreditLimit gt 10000&$top=10

3. MCP Server calls the D365 F&O endpoint with this query.
4. Response is then passed back to the LLM for post-processing to extract just the Customer Name and ID.
5. Final cleaned output is returned to the caller.

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💡 LLM Prompting Strategy

Two distinct prompts are used:

• Prompt #1 (Generate OData Query) – Guides the LLM to only return a valid OData URL fragment.
• Prompt #2 (Refine Response) – Extracts only CustomerAccount and CustomerName from the raw OData response.

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📂 Getting Started

1. Clone the repo.
2. Copy .env.sample to .env and fill in your keys.
3. Run the server:

uvicorn main:app --reload

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🔐 Security

Make sure .env is added to .gitignore. Do not commit your credentials or secrets.

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Testing

Use CURL or tools like PostMan to send API call: Invoke-RestMethod -Uri http://127.0.0.1:8000/api/mcp -Method POST -Body '{"name":"Test","context":"Get Customers and first only"}' ` -ContentType "application/json"

📄 License

MIT License