Azure AI Foundry Databricks Research
Agentic AI solution for exploring and reasoning over Databricks using Azure AI Foundry, Semantic Kernel, OpenAPI, and MCP with full observability and orchestration support.
Install
$ agentstack add mcp-jonathanscholtes-azure-ai-foundry-databricks-research ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Links to your public security report.[](https://agentstack.voostack.com/security/report/mcp-jonathanscholtes-azure-ai-foundry-databricks-research) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
> ⚠️ > This project is currently in active development and may contain breaking changes. > Updates and modifications are being made frequently, which may impact stability or functionality. This notice will be removed once development is complete and the project reaches a stable release.
Azure AI Foundry Agent Development for Exploring and Reasoning over Databricks
Overview
This project demonstrates how to explore and reason over structured data (Databricks) using the Azure AI Foundry Agent Service and the Semantic Kernel Agentic Framework.
It includes:
- Python function tool calling
- OpenAPI endpoints hosted on Azure App Service
- Dynamic tool resolution with the Model Context Protocol (MCP), deployed as an HTTP-Streamable remote server on Azure Container Apps
The solution also highlights observability by leveraging Azure AI Foundry Tracability and Evaluation SDKs for deeper insights into agent behavior. Additionally, it demonstrates agent orchestration with the Semantic Kernel Orchestration Framework, enabling advanced reasoning steps for data exploration.
Key Features
- Agentic AI with Azure AI Agent Service and Semantic Kernel
Build and experiment with prebuilt or custom agents that can delegate tasks, make decisions, and interact with multiple endpoints (tools). Explore orchestration patterns using the Semantic Kernel framework.
- Integration with OpenAPI and MCP
Extend agents to external services, such as Azure Databricks, to perform actions like retrieving live data, triggering workflows, or interacting with apps and systems. MCP enables defining tools once and exposing them consistently across scalable, modular architectures.
🛠️ Core Steps for Solution Implementation
Follow these key steps to successfully deploy and configure the solution:
1️⃣ [Deploy the Solution](docs/deployment.md)
- Instructions for deploying solution, including prerequisites, configuration steps.
2️⃣ [Hands-On with Agents](docs/notebooks.md)
- Work through practical notebooks that demonstrate agents built to talk with your data and generate insights through analysis.
♻️ Clean-Up
After completing the workshop and testing, ensure you delete any unused Azure resources or remove the entire Resource Group to avoid additional charges.
📜 License
This project is licensed under the [MIT License](LICENSE.md), granting permission for commercial and non-commercial use with proper attribution.
Disclaimer
This workshop and demo application are intended for educational and demonstration purposes. It is provided "as-is" without any warranties, and users assume all responsibility for its use.
Source & license
This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: jonathanscholtes
- Source: jonathanscholtes/Azure-AI-Foundry-Databricks-Research
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.