# Contract Risk Mcp Foundry > An always-on, autonomous agentic risk platform demonstrating event-driven AI agents on Azure for continuous FX and interest rate contract risk monitoring. - **Type:** MCP server - **Install:** `agentstack add mcp-jonathanscholtes-contract-risk-mcp-foundry` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [jonathanscholtes](https://agentstack.voostack.com/s/jonathanscholtes) - **Installs:** 0 - **Category:** [Cloud & Infrastructure](https://agentstack.voostack.com/c/cloud-infrastructure) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [jonathanscholtes](https://github.com/jonathanscholtes) - **Source:** https://github.com/jonathanscholtes/contract-risk-mcp-foundry - **Website:** https://stochasticcoder.com/2026/01/29/from-chatbots-to-autonomous-agents-building-an-always-on-risk-platform/ ## Install ```sh agentstack add mcp-jonathanscholtes-contract-risk-mcp-foundry ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # Autonomous Contract Risk Platform ## 🚀 Autonomous Agent Pattern This project demonstrates an **autonomous AI system** that continuously analyzes contracts for risk using a multi-agent pipeline. Unlike traditional chatbots, this system is **always-on**: - Agents monitor and process documents continuously - No user prompt required - Risk signals are identified and evaluated in real time 👉 This is a practical implementation of the shift from **chatbots → autonomous agents** ## 🧠 Deep Dive This repository is part of a full architectural walkthrough: 👉 [From Chatbots to Autonomous Agents: Building an Always-On Risk Platform](https://stochasticcoder.com/2026/01/29/from-chatbots-to-autonomous-agents-building-an-always-on-risk-platform/) Covers: - Why autonomous agents matter - System design and trade-offs - How always-on pipelines differ from request/response systems An **autonomous agentic system** demonstrating production-ready AI agent orchestration for continuous financial contract risk monitoring. **Built with:** - **Microsoft Azure AI Foundry** - Autonomous agent hosting and orchestration - **MCP (Model Context Protocol)** - Standardized tool-calling interface - **Azure Kubernetes Service (AKS)** - Scalable compute and event orchestration - **Azure DocumentDB (MongoDB)** - Flexible contract and risk memo storage - **RabbitMQ** - Asynchronous job queue for risk calculations - **Grafana/Prometheus/OpenTelemetry** - Production-grade observability ## How it works The system runs as a continuous pipeline: 1. Ingest contracts 2. Analyze risk signals 3. Evaluate findings 4. Produce structured output Agents operate continuously rather than responding to prompts. ## 🎯 Overview This platform demonstrates an **autonomous agentic system** that continuously monitors financial contracts for **FX and Interest Rate risk**. The system operates without human intervention, automatically: - **Detecting events**: Market shocks, threshold breaches, scheduled scans - **Invoking AI agents**: Foundry agents autonomously analyze risk exposure - **Calculating risk metrics**: FX VaR, IR DV01, stress tests via async workers - **Generating insights**: AI-written risk memos with hedge recommendations - **Scaling dynamically**: Workers autoscale based on workload (KEDA) - **Full observability**: Real-time metrics, distributed tracing, dashboards **Key Architectural Pattern**: Event-driven orchestration where AKS detects events and invokes Foundry agents, which coordinate workflows using MCP tools. Agents operate with **controlled autonomy** - they make decisions within defined boundaries while maintaining full auditability. **Built with:** - **Microsoft Foundry** - Autonomous agent hosting and orchestration - **MCP (Model Context Protocol)** - Standardized tool-calling interface - **Azure Kubernetes Service (AKS)** - Scalable compute and event orchestration - **Azure DocumentDB (MongoDB)** - Flexible contract and risk memo storage - **RabbitMQ** - Asynchronous job queue for risk calculations - **Grafana/Prometheus/OpenTelemetry** - Production-grade observability ## 📐 Architecture --- ### Components **AKS Orchestration Layer** (Event Detection & Scheduling) - `agent-orchestrator`: Event-driven agent invocation service - Detects events (RabbitMQ messages, market shocks, threshold breaches) - Runs cron-based portfolio scans and market data updates - Invokes Foundry agents via API - Provides controlled autonomy with full observability **Foundry Agents** (Hosted in Microsoft Foundry) - **ThresholdBreachAnalyst**: Triggered when risk metrics exceed thresholds (FX VaR > $100k or IR DV01 > $50k). Analyzes breach severity, generates hedge recommendations, and persists risk memos. - **MarketShockAnalyst**: Triggered on significant market movements (e.g., EURUSD drops 2.5%). Identifies exposed contracts, recalculates portfolio risk, and aggregates impact analysis. - **PortfolioScanAnalyst**: Runs on schedule (daily 8 AM UTC, every 4 hours intraday). Performs comprehensive portfolio scans, generates executive summaries, and identifies top-risk contracts. - All agents coordinate workflows via MCP tools and are fully auditable **MCP Tool Servers** (Data & Job Submission Layer) - `mcp-contracts`: Contract registry and risk memo storage - `mcp-market`: Market data snapshots (FX rates, volatility) - `mcp-risk`: Risk job submission and result retrieval **AKS Workers** (Compute Layer) - `risk-worker`: Executes FX VaR, IR DV01, and stress tests - Consumes jobs from RabbitMQ - Autoscales based on queue depth (KEDA) **Infrastructure** - `MongoDB (DocumentDB)`: Flexible storage for contracts and risk memos - `RabbitMQ`: Job/result queues with DLQ support - `Prometheus`: Metrics collection - `Grafana`: Risk operations dashboards - `OpenTelemetry`: Distributed tracing ### 📋 Event-Driven Workflow How the system executes end-to-end **Event-Driven Agent Invocation:** 1. **AKS orchestrator** detects event: - Cron schedule triggers daily portfolio scan - Risk result exceeds threshold - Market shock detected - New contract added 2. **Orchestrator invokes Foundry agent** via API with context: - Event details and parameters - MCP endpoint URLs for tool access 3. **Foundry agent** executes workflow: - Calls `contracts.search_contracts()` to get portfolio - Calls `risk.run_fx_var()` for each contract → returns `job_id` - Polls `risk.get_risk_result(job_id)` until complete - Analyzes results and generates recommendations - Calls `contracts.write_risk_memo()` to persist analysis 4. **Risk workers** process async jobs: - Consume from RabbitMQ - Compute VaR/DV01 - Publish results back to queue 5. **Orchestrator** monitors results: - Detects threshold breaches - Triggers follow-up agent invocations if needed 6. **Grafana** shows real-time metrics and alerts ## 📁 Project Structure ``` contract-risk-mcp-foundry/ ├── apps/ # Application services │ ├── agent-orchestrator/ # Event-driven agent invocation │ ├── mcp-contracts/ # Contract registry MCP server │ ├── mcp-risk/ # Risk job submission MCP server │ ├── mcp-market/ # Market data MCP server │ └── risk-worker/ # Risk calculation worker ├── shared/ # Shared Pydantic schemas │ └── contracts/ # Contract, job, result models ├── k8s/ # Kubernetes resources │ └── helm/ # Helm charts │ ├── platform/ # Monitoring stack │ ├── mcp-tools/ # MCP servers │ └── risk-workers/ # Workers + KEDA ├── observability/ # Observability configs │ ├── grafana/ # Dashboards and datasources │ ├── prometheus/ # Prometheus config │ └── otel/ # OpenTelemetry config ├── scripts/ # Deployment and testing scripts └── infra/ # Bicep infrastructure ``` ## 🚀 Deployment ### **Prerequisites** Ensure you have the following before deploying the solution: - ✅ **Azure Subscription:** Active subscription with sufficient privileges to create and manage resources. - ✅ **Azure CLI:** Install the [Azure CLI](https://learn.microsoft.com/en-us/cli/azure/get-started-with-azure-cli) for managing Azure resources. - ✅ **IDE with Bicep & PowerShell Support:** Use [VS Code](https://code.visualstudio.com/download) with the **Bicep extension** for development and validation. - ✅ `kubectl`, `helm` - Python 3.10+ --- ### **1. Clone the Repository** Clone the project repository to your local machine: ```bash git clone https://github.com/jonathanscholtes/contract-risk-mcp-foundry cd contract-risk-mcp-foundry ``` ### 2. Deploy the Solution Use the following PowerShell command to deploy the solution. Be sure to replace the placeholders with your actual subscription name and Azure region. ```powershell .\deploy.ps1 ` -Subscription 'YOUR_SUBSCRIPTION_NAME' ` -Location 'eastus2' ` -UserObjectId 'YOUR_USER_OBJECT_ID' ``` **Parameter Details:** - `Subscription`: Your Azure subscription name (required) - `Location`: Azure region for resources, default is `eastus2` (optional) - `UserObjectId`: (Optional) Your Azure AD user object ID for AKS cluster admin access. Get it via: ```powershell az ad signed-in-user show --query id -o tsv ``` This deployment will: - ✅ Deploy Azure infrastructure (AKS, MongoDB, AI Foundry, ACR) - ✅ Build and push container images - ✅ Deploy MCP tool servers to AKS - ✅ Deploy three autonomous agents to Microsoft Foundry (ThresholdBreachAnalyst, MarketShockAnalyst, PortfolioScanAnalyst) - ✅ Configure RabbitMQ, risk workers, and auto-scaling (KEDA) - ✅ Set up monitoring stack (Prometheus, Grafana, OpenTelemetry) - ✅ Output service endpoints (Grafana, MCP servers, etc.) ## 📊 Next Steps After deployment completes, the script displays service endpoints: ``` === Service Endpoints === Grafana: http:// MCP Contracts: http:// MCP Risk: http:// MCP Market: http:// ``` ### 1. Access Grafana Dashboards Navigate directly to the Grafana endpoint from the deployment output: ``` http:// ``` **Default credentials:** `admin` / `admin` The **Contract Risk Operations Center - Enhanced** dashboard is pre-deployed and shows: - Real-time risk metrics and contract VaR values - Job submission and completion rates - Pending jobs gauge - Agent invocation logs ### 2. Monitor Agent Execution Agent logs are available in Azure AI Foundry: 1. Go to [Azure AI Foundry](https://ai.azure.com/) 2. Select your project 3. Navigate to **Agents** section 4. Click each agent (ThresholdBreachAnalyst, MarketShockAnalyst, PortfolioScanAnalyst) to view execution history and logs ## ♻️ **Clean-Up** After completing testing or when no longer needed, 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 CODE IS PROVIDED FOR EDUCATIONAL AND DEMONSTRATION PURPOSES ONLY.** This sample code is not intended for production use and is provided "AS IS", without warranty of any kind, express or implied, including but not limited to the warranties of merchantability, fitness for a particular purpose, and noninfringement. In no event shall the authors or copyright holders be liable for any claim, damages, or other liability, whether in an action of contract, tort, or otherwise, arising from, out of, or in connection with the software or the use or other dealings in the software. **Key Points:** - This is a **demonstration project** showcasing autonomous agentic architecture patterns - **Not intended for production financial risk management** without significant additional development, testing, and compliance review - Risk calculations are simplified models for demonstration purposes only - Users are responsible for ensuring compliance with applicable regulations and security requirements - Microsoft Azure services incur costs - monitor your usage and clean up resources when done - No warranties or guarantees are provided regarding accuracy, reliability, or suitability for any purpose By using this code, you acknowledge that you understand these limitations and accept full responsibility for any consequences of 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](https://github.com/jonathanscholtes) - **Source:** [jonathanscholtes/contract-risk-mcp-foundry](https://github.com/jonathanscholtes/contract-risk-mcp-foundry) - **License:** MIT - **Homepage:** https://stochasticcoder.com/2026/01/29/from-chatbots-to-autonomous-agents-building-an-always-on-risk-platform/ Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/mcp-jonathanscholtes-contract-risk-mcp-foundry - Seller: https://agentstack.voostack.com/s/jonathanscholtes - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.