What is MCP Gateway? Azure IoT Edge Functionality Explained

TL;DR

1. An MCP Gateway is a specialized edge gateway, often powered by platforms like Azure IoT Edge, designed to implement the Model Context Protocol (MCP).

2. It serves as a crucial bridge, enabling AI agents and cloud services to interact with diverse industrial and IoT devices at the edge through standardized, context-rich APIs.

3. Azure IoT Edge provides the robust runtime, module management, and cloud connectivity essential for deploying, managing, and scaling MCP Gateway functionalities.

4. Key capabilities include protocol translation, local data processing for AI inference, enhanced edge security, and resilient offline operations.

5. By bringing advanced intelligence and structured communication to the edge, MCP Gateways accelerate AI deployments, reduce latency, and ensure data privacy in distributed environments.

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The proliferation of IoT devices and the rapid ascent of AI agents are transforming how industries operate, pushing computational power and intelligence closer to the data source—the edge. This shift, however, introduces complex challenges around data ingestion, protocol diversity, and secure communication, especially when intelligent systems need to interact seamlessly with physical assets. Navigating this intricate landscape requires a new breed of edge infrastructure. This blog delves into the concept of the MCP Gateway and how Azure IoT Edge provides a robust, scalable platform to unlock its full potential, ensuring that your edge deployments are not just connected, but truly intelligent and responsive.

What is an MCP Gateway? Deciphering the Model Context Protocol at the Edge

In the evolving world of distributed intelligence, the term MCP Gateway refers to a sophisticated edge gateway designed to implement and leverage the Model Context Protocol (MCP). At its core, MCP is a framework that enables AI agents, services, and applications to interact with APIs in a more structured, contextual, and intelligent manner. An MCP Gateway, therefore, acts as a crucial intermediary, translating the diverse, often proprietary, languages of operational technology (OT) and IoT devices into a standardized, machine-readable format that AI models and cloud services can easily understand and act upon.

Unlike traditional IoT gateways that primarily focus on data ingestion and basic connectivity, an MCP Gateway elevates the edge by:

1. **Standardizing Interactions:** It exposes device capabilities and data streams as well-defined APIs, making them accessible to autonomous AI agents.
2. **Providing Context:** It enriches raw device data with operational context, ensuring AI models receive relevant information for accurate decision-making.
3. **Facilitating Agentic AI:** By adhering to the principles of the Model Context Protocol, it helps bridge the gap between AI agents and the physical world, allowing agents to understand, interact with, and orchestrate edge resources effectively.
4. **Enabling Autonomous Operations:** It allows for local processing and decision-making, reducing reliance on constant cloud connectivity and improving real-time responsiveness.

Essentially, an MCP Gateway transforms the edge from a mere data collection point into an intelligent, active participant in the broader AI ecosystem, making edge devices not just "connected," but "understandable" and "actionable" for advanced AI systems.

Azure IoT Edge: The Foundational Platform for MCP Gateway Functionality

Azure IoT Edge is a managed service that deploys your cloud workloads to run on edge devices. It is an ideal platform for implementing an MCP Gateway due to its inherent capabilities for modularity, local processing, and seamless cloud integration. IoT Edge extends the intelligence and analytics of the cloud directly to edge devices, enabling them to act more autonomously and respond in real-time. It achieves this through:

1. **IoT Edge Runtime:** A lightweight, robust runtime that runs on edge devices, allowing them to communicate with Azure IoT Hub and manage the deployment of modules.
2. **IoT Edge Modules:** Docker-compatible containers that encapsulate services and applications. These can be custom code, Azure services (like Stream Analytics or Machine Learning), or third-party solutions. This modularity is key for building flexible MCP Gateway functionalities.
3. **IoT Edge Hub:** A local message broker that routes communications between modules, the local device, and IoT Hub in the cloud. It supports offline operations by caching messages locally.
4. **IoT Edge Agent:** Manages module deployment, monitoring, and reporting, ensuring modules are running correctly and updating them from the cloud as needed.

By providing a secure, scalable, and manageable environment, Azure IoT Edge creates the perfect substrate for hosting the complex functionalities required by an MCP Gateway. It allows developers to build and deploy intelligent applications that process data close to its source, minimizing latency, conserving bandwidth, and enhancing security, all while maintaining centralized cloud management.

Bridging the Gap: How Azure IoT Edge Enables MCP Gateway Capabilities

Azure IoT Edge doesn't just host an MCP Gateway; it actively enables its core capabilities through its architectural design and feature set. The synergy between Azure IoT Edge and the MCP Gateway concept creates a powerful solution for intelligent edge deployments:

1. Module-Based Implementation of MCP Logic

With Azure IoT Edge, the specific logic required for an MCP Gateway can be encapsulated within one or more IoT Edge modules. These modules can be custom-developed to:

• **Process Contextual Data:** Modules can enrich raw device data with environmental context, historical trends, or metadata, transforming it into meaningful information for AI agents.
• **Implement MCP Specifications:** Specific modules can handle the parsing and generation of MCP-compliant messages, ensuring standardized communication protocols for AI interactions.
• **Expose Edge APIs:** IoT Edge modules can run local web servers or API endpoints, effectively exposing device functionalities and processed data as API Gateway services at the edge. This is crucial for making edge capabilities accessible in a structured way to AI agents and other edge services.

2. Protocol Adaptation and Ingestion

A primary function of an MCP Gateway is to unify disparate communication protocols. Azure IoT Edge excels here by allowing custom modules to:

• **Connect Diverse Devices:** Modules can be developed to speak industrial protocols like Modbus, OPC UA, CAN bus, or proprietary device protocols.
• **Translate Data:** These modules ingest data from various sources, normalize it, and translate it into a common format (e.g., JSON) before applying MCP context.
• **Local Pre-processing:** Data can be filtered, aggregated, and transformed right at the edge, reducing the volume of data sent to the cloud and improving the efficiency of downstream AI processing.

3. Local Intelligence and AI Inference

The "Model" in Model Context Protocol highlights the integration of AI. Azure IoT Edge allows you to deploy machine learning models (e.g., from Azure Machine Learning) as modules to the edge. An MCP Gateway hosted on IoT Edge can then:

• **Perform Real-time Inference:** Execute AI models locally for tasks like predictive maintenance, quality control, or anomaly detection.
• **Generate Context for AI Agents:** The output of these local AI models can feed directly into the contextual information provided to AI agents, enhancing their decision-making.
• **Act Autonomously:** Based on local inference, the gateway can trigger immediate actions on devices without waiting for cloud directives, enabling true edge autonomy.

4. Enhanced Security and Access Control

Security at the edge is paramount. Azure IoT Edge provides a secure foundation for an MCP Gateway through:

• **Secure Module Deployment:** Modules are securely deployed and updated from IoT Hub, with built-in mechanisms for integrity checks.
• **Managed Identities:** Modules can use managed identities for secure access to Azure cloud services without embedding credentials.
• **Local Authentication and Authorization:** An MCP Gateway module can implement fine-grained access control for local APIs, ensuring that only authorized AI agents or services can interact with specific edge resources. This extends API security to the very edge of your network.

5. Offline Capabilities and Resilience

Many edge environments have intermittent or unreliable connectivity. Azure IoT Edge's offline capabilities are critical for an MCP Gateway:

• **Local Storage and Forwarding:** The IoT Edge Hub can store messages locally and forward them to the cloud once connectivity is restored.
• **Autonomous Operation:** Local modules, including the MCP Gateway logic and AI models, can continue to operate and make decisions even when disconnected from the cloud, ensuring operational continuity.

By leveraging these powerful features of Azure IoT Edge, an MCP Gateway can effectively unify diverse edge data, embed local intelligence, secure communications, and expose structured APIs, transforming raw device interactions into intelligent, actionable contexts for AI agents and cloud applications.

Key Functionalities of an MCP Gateway on Azure IoT Edge

An MCP Gateway operating on Azure IoT Edge offers a comprehensive suite of functionalities designed to facilitate intelligent interactions between AI agents and the physical world. These capabilities are crucial for modern industrial and enterprise edge deployments:

1. Standardized API Exposure for AI Agents

The MCP Gateway transforms raw device data and operational capabilities into structured, discoverable APIs. This means that instead of direct, often complex device communication, AI agents or cloud services can interact with edge resources via standard HTTP/REST or other defined interfaces. This functionality is pivotal for making existing APIs and services MCP-ready, enabling them to be consumed by intelligent agents. It effectively acts as a local API Management layer for the edge.

2. Model Context Protocol (MCP) Implementation

At the heart of the MCP Gateway is its ability to implement the Model Context Protocol. This involves:

• **Contextual Data Generation:** Enriching data streams with relevant metadata, operational states, environmental conditions, and historical context.
• **Semantic Mapping:** Translating device-specific terminology and data formats into a common, semantically rich model that AI agents can universally understand.
• **Actionable Insights:** Presenting data and device capabilities in a way that directly informs an AI agent's decision-making process and potential actions.

3. Protocol Adaptation and Ingestion

The industrial landscape is rife with heterogeneous protocols. An MCP Gateway on IoT Edge addresses this by:

• **Multi-Protocol Support:** Connecting to a vast array of legacy and modern devices using protocols like Modbus, OPC UA, MQTT, CAN bus, and custom serial interfaces.
• **Data Normalization:** Ingesting data from these diverse sources and normalizing it into a consistent format for internal processing and external API exposure.
• **Edge Filtering:** Applying initial filters to data streams to reduce noise and transmit only relevant information, optimizing bandwidth.

4. Real-time Data Processing & Analytics at the Edge

Leveraging the computational power of IoT Edge, the MCP Gateway can perform significant local processing:

• **AI/ML Inference:** Hosting and executing pre-trained machine learning models for real-time anomaly detection, predictive analytics, or classification tasks.
• **Complex Event Processing:** Identifying patterns or sequences of events that trigger specific actions or alerts without needing cloud roundtrips.
• **Local Data Aggregation:** Aggregating data over time or from multiple sources locally before sending summaries to the cloud.

5. Enhanced Security & Access Control

Given its critical role, the MCP Gateway integrates robust security measures:

• **Secure Boot & Device Identity:** Leveraging hardware-backed security features of the edge device.
• **Authentication & Authorization:** Implementing strict controls for who (which AI agent, which user) can access specific device data or trigger actions via its exposed APIs, ensuring API gateway security at the edge.
• **Data Encryption:** Encrypting data at rest and in transit between the gateway, devices, and the cloud.
• **Firewall & Network Isolation:** Isolating the OT network from external threats.

6. Offline Capabilities & Resilience

Edge deployments often face unreliable network conditions. The MCP Gateway on IoT Edge is designed for resilience:

• **Local Caching:** Storing data and critical operational parameters locally to ensure continuity during network outages.
• **Autonomous Operations:** Maintaining local decision-making and control over devices even when disconnected from the cloud.
• **Store-and-Forward:** Reliably queuing data and events to be sent to the cloud once connectivity is restored.

7. Efficient Resource Utilization

Edge devices often have constrained resources. The MCP Gateway helps optimize this:

• **Bandwidth Optimization:** Reducing data volume sent to the cloud through local processing, filtering, and aggregation.
• **Compute Optimization:** Running AI models efficiently on limited hardware, ensuring maximum utilization.
• **Dynamic Resource Allocation:** IoT Edge allows for dynamic allocation of resources to modules based on demand.

These functionalities collectively enable the MCP Gateway to act as an intelligent, secure, and resilient bridge, crucial for integrating edge operations with advanced AI and cloud intelligence.

Benefits of Deploying MCP Gateways with Azure IoT Edge

The combination of an MCP Gateway and Azure IoT Edge delivers a compelling set of advantages for organizations venturing into intelligent edge computing:

1. **Accelerated AI Deployment at the Edge:** By providing a standardized, context-rich API surface, the MCP Gateway significantly simplifies the integration of AI models and agents with edge devices. Azure IoT Edge's modular design and cloud integration capabilities allow for rapid deployment and updates of these AI components.
2. **Reduced Latency and Improved Real-time Responsiveness:** Processing data and making decisions at the edge eliminates the roundtrip to the cloud, drastically reducing latency. This is critical for applications requiring immediate action, such as industrial automation, autonomous vehicles, or patient monitoring.
3. **Enhanced Data Privacy and Compliance:** Local processing reduces the need to send all raw data to the cloud, allowing sensitive information to remain within the local environment. This helps organizations meet stringent data residency and privacy regulations.
4. **Optimized Bandwidth and Cost Savings:** Filtering, aggregating, and processing data at the edge means less data needs to be transmitted to the cloud. This reduces bandwidth consumption and associated cloud ingress/egress costs, particularly beneficial for remote locations with expensive or limited connectivity.
5. **Increased Operational Resiliency:** The ability of MCP Gateways on IoT Edge to operate autonomously, even during network outages, ensures business continuity for critical operations, mitigating the impact of intermittent connectivity.
6. **Simplified Management and Scalability:** Azure IoT Edge provides a unified control plane for deploying, monitoring, and updating modules across thousands of edge devices. This central management capability simplifies the scaling of MCP Gateway functionalities across distributed environments. This level of management is often a key aspect of broader API Management Platforms.
7. **Interoperability Across Diverse Systems:** By translating proprietary device protocols into a standardized, MCP-compliant format, the gateway breaks down silos between different vendors and technologies, fostering greater interoperability within the edge ecosystem.
8. **Improved API Governance and Control:** It allows for the enforcement of consistent API policies, access controls, and data standards directly at the edge, ensuring secure and compliant interactions with intelligent agents.

Real-World Applications and Use Cases

The power of an MCP Gateway on Azure IoT Edge becomes evident in a variety of industry verticals:

1. Manufacturing and Industrial Automation

• **Predictive Maintenance:** An MCP Gateway collects data from machinery (vibration, temperature, pressure sensors), performs local AI inference to detect anomalies, and exposes a standardized API for maintenance agents to query machine health and schedule interventions. This reduces downtime and operational costs.
• **Quality Control:** Real-time visual inspection systems at the edge can use local AI models to identify product defects. The gateway can expose defect counts and types via MCP-compliant APIs for production management systems or automated robotic agents to take corrective actions.
• **Energy Optimization:** Managing energy consumption in a factory by correlating machine usage, production schedules, and local energy prices, allowing an AI agent to optimize power usage without constant cloud interaction.

2. Smart Cities and Infrastructure

• **Intelligent Traffic Management:** Gateways at intersections collect data from cameras and sensors (traffic flow, pedestrian density). Local AI models on the MCP Gateway can dynamically adjust traffic signals. The gateway exposes APIs for city management systems to monitor traffic patterns and override controls.
• **Environmental Monitoring:** Collecting air quality, noise, and weather data from distributed sensors. An MCP Gateway aggregates and analyzes this data, providing contextualized insights to smart city applications and emergency services.

3. Healthcare and Remote Monitoring

• **Remote Patient Monitoring:** Wearable devices and home sensors transmit vital signs. An MCP Gateway in the home processes this data locally, using AI to detect critical changes and alert caregivers via an MCP-enabled API, ensuring data privacy and reducing cloud load.
• **Medical Equipment Management:** Monitoring the status and performance of medical devices within a hospital. The MCP Gateway facilitates communication between devices, hospital IT systems, and maintenance scheduling agents.

4. Retail and Supply Chain

• **Smart Inventory Management:** Sensors on shelves track product levels. An MCP Gateway uses local AI to detect low stock, trigger reorder alerts, and expose this information to inventory management systems or autonomous robots for restocking.
• **Personalized Customer Experience:** Analyzing in-store behavior data (anonymously) to provide real-time, personalized recommendations or promotions via an MCP Gateway API to point-of-sale systems or customer-facing applications.

5. Agriculture

• **Precision Farming:** MCP Gateways in the field collect data from soil sensors, weather stations, and drone imagery. Local AI advises on optimal irrigation, fertilization, and pest control, exposing recommendations to farm management systems or automated machinery.

These examples illustrate how the MCP Gateway on Azure IoT Edge serves as a critical enabler for sophisticated, real-time, and autonomous operations across a multitude of industries, pushing the boundaries of what's possible at the edge.

Architectural Considerations and Implementation for MCP Gateways on Azure IoT Edge

Implementing an MCP Gateway on Azure IoT Edge involves careful design and adherence to best practices to ensure scalability, security, and maintainability. Here are key architectural and implementation considerations:

1. IoT Edge Modules as Building Blocks

The core functionality of your MCP Gateway will reside in custom IoT Edge modules. Each module should ideally encapsulate a specific function:

• **Protocol Adapters:** Modules dedicated to connecting to specific industrial protocols (e.g., Modbus TCP, OPC UA client).
• **Data Pre-processors:** Modules for filtering, aggregating, or transforming raw data.
• **AI/ML Inference Modules:** Modules hosting your machine learning models for local analysis.
• **MCP Logic Module:** A central module responsible for implementing the Model Context Protocol, exposing APIs, and coordinating interactions between other modules.
• **Local API Server Module:** A lightweight web server module (e.g., using Flask or Node.js) to expose MCP-compliant APIs for local and cloud consumption.

2. Containerization and Deployment Manifests

All MCP Gateway components are containerized (Docker images). Azure IoT Edge uses deployment manifests (JSON files) to define:

• Which modules to deploy to which edge devices.
• Module configurations, environment variables, and create options.
• Routing rules between modules, the local IoT Edge Hub, and Azure IoT Hub.

This declarative approach ensures consistent deployments and simplifies updates across a fleet of gateways.

3. Secure Communication and Access Control

• **TLS/SSL:** All communication between modules, and between the gateway and IoT Hub, should be secured with TLS/SSL.
• **Module Identity:** Each IoT Edge module gets a unique identity from IoT Hub, which can be used for authentication.
• **Role-Based Access Control (RBAC):** For local APIs exposed by the MCP Gateway, implement RBAC or API key management to control which AI agents or services can access specific data or functionalities. This is a critical aspect of API governance at the edge.
• **Network Segmentation:** Isolate your OT network from IT networks where possible, with the gateway acting as a controlled bridge.

4. Integration with Azure Cloud Services

While the MCP Gateway operates at the edge, it remains tightly integrated with Azure cloud services:

• **Azure IoT Hub:** The central management plane for devices, modules, and data routing.
• **Azure Machine Learning:** For developing, training, and deploying AI models to the edge modules.
• **Azure Stream Analytics/Functions:** For further processing of aggregated data in the cloud or triggering serverless workflows.
• **Azure Data Explorer/Storage:** For long-term data storage and advanced analytics.

5. Monitoring and Observability

Implement robust API monitoring for your MCP Gateway to track its health, performance, and data flow:

• **Azure Monitor:** Collect metrics and logs from IoT Edge runtime and modules.
• **Custom Metrics:** Implement custom metrics within your MCP Gateway modules to track specific operational parameters or API usage.
• **Distributed Tracing:** For complex module interactions, consider implementing distributed tracing to diagnose issues.

6. API Lifecycle Management at the Edge

Treat the APIs exposed by your MCP Gateway as products. This means having a strategy for versioning, documentation, and deprecation. While it's at the edge, maintaining a clear API design and consistent lifecycle is crucial, similar to managing any cloud API.

7. Edge Device Considerations

• **Hardware Selection:** Choose edge hardware that meets the computational demands of your AI models and protocol adapters.
• **Operating System:** Azure IoT Edge supports Linux and Windows operating systems.
• **Network Connectivity:** Plan for robust and resilient network connectivity for cloud communication, even if the gateway can operate offline.

By thoughtfully addressing these architectural and implementation points, organizations can build powerful, secure, and manageable MCP Gateway solutions that unlock true intelligence at the edge.

The Future Landscape: MCP Gateways and Agentic AI

The concept of the MCP Gateway is not just a present-day solution; it is a vital component in the future evolution of distributed intelligence and agentic AI. As AI agents become more sophisticated and autonomous, their ability to seamlessly interact with and understand the physical world will be paramount. An MCP Gateway, underpinned by platforms like Azure IoT Edge, provides this crucial link.

Imagine a future where:

• **Self-Optimizing Systems:** AI agents communicate through MCP Gateways to dynamically orchestrate entire industrial processes, from supply chain logistics to manufacturing lines, reacting to real-time data and optimizing for efficiency, cost, and sustainability. This represents a complex form of API orchestration at the very edge.
• **Hyper-Personalized Experiences:** In retail or smart homes, AI agents leverage contextual data from MCP Gateways to offer truly personalized services, anticipating needs and automating tasks with minimal human intervention.
• **Resilient and Adaptive Infrastructure:** Critical infrastructure, from energy grids to transportation networks, relies on MCP Gateways to manage localized intelligence, predict failures, and self-heal, ensuring continuous operation even in the face of disruptions.

The role of the MCP Gateway will expand to include more advanced capabilities such as federated learning at the edge, where AI models can be trained on local data without it ever leaving the device, further enhancing privacy and efficiency. Its ability to expose a standardized, semantically rich API surface for edge devices will also be critical for integrating with nascent technologies like decentralized autonomous organizations (DAOs) and blockchain-based IoT solutions, driving trust and transparency.

The combination of the MCP Gateway with the robust capabilities of Azure IoT Edge positions organizations to not only harness the current wave of edge AI but also to confidently navigate the complexities and opportunities presented by the next generation of intelligent, autonomous systems. It is the bridge that connects the physical world's vast data with the transformative power of AI, right where it matters most: at the edge.

FAQs

1. What exactly is an MCP Gateway?

An MCP Gateway is a specialized edge gateway, often implemented using platforms like Azure IoT Edge, that facilitates the Model Context Protocol (MCP). Its primary role is to standardize and contextualize interactions between diverse industrial/IoT devices at the edge and intelligent AI agents or cloud services, providing a structured API surface for machine-to-machine communication with rich contextual information.

2. How does Azure IoT Edge support MCP Gateway functionality?

Azure IoT Edge provides the essential runtime, module management, and cloud connectivity needed to deploy an MCP Gateway. Its modular architecture allows developers to encapsulate MCP logic, protocol adapters, and AI inference models into deployable containers (IoT Edge modules), which can then run autonomously on edge devices, processing data locally and exposing APIs for interaction.

3. What are the main benefits of using an MCP Gateway with Azure IoT Edge?

The key benefits include accelerating AI deployment at the edge, reducing latency for real-time operations, enhancing data privacy and compliance, optimizing bandwidth and cost, increasing operational resiliency through offline capabilities, and simplifying management and scalability across distributed environments. It also promotes interoperability by standardizing diverse device protocols.

4. What kind of devices or protocols can an MCP Gateway connect to?

An MCP Gateway on Azure IoT Edge can connect to a wide array of devices and protocols, including legacy industrial systems (Modbus, OPC UA), modern IoT sensors (MQTT, AMQP), and proprietary devices via custom protocol adapter modules. It acts as a universal translator, normalizing data from these sources into a consistent, MCP-compliant format.

5. How does an MCP Gateway contribute to AI agent capabilities?

An MCP Gateway empowers AI agents by providing them with a standardized, context-rich, and actionable view of edge resources. Instead of raw, uninterpreted data, agents receive semantically meaningful information and can interact with devices via well-defined APIs, enabling more intelligent decision-making, autonomous operations, and effective orchestration of physical assets.

6. Is the MCP Gateway primarily for industrial use cases?

While industrial automation (manufacturing, energy) is a prominent use case due to its complex device ecosystems and real-time demands, MCP Gateways are beneficial wherever intelligent systems need to interact with physical environments. This includes smart cities, healthcare, retail, and agriculture, wherever local intelligence, standardized APIs, and context for AI agents are critical.

7. What is the difference between an MCP Gateway and a traditional API Gateway?

A traditional API Gateway typically manages and secures APIs for cloud-based microservices or web applications, focusing on routing, authentication, and rate limiting. An MCP Gateway, on the other hand, is specialized for edge environments, adding capabilities like industrial protocol translation, local AI inference, context generation for AI agents, and robust offline operation. It extends cloud-native microservices and Kubernetes like principles to the distributed edge.

8. How does an MCP Gateway handle security at the edge?

An MCP Gateway leverages Azure IoT Edge's built-in security features, such as secure boot, device identity, and managed identities for modules. Additionally, it implements local authentication and authorization for its exposed APIs, ensuring that only trusted AI agents or services can interact with specific edge resources. Data encryption and network isolation are also crucial components for a comprehensive API security posture at the edge.

9. Can an MCP Gateway operate fully offline?

Yes, a key strength of an MCP Gateway on Azure IoT Edge is its ability to operate autonomously in offline or intermittently connected environments. The IoT Edge Hub can store messages locally, and local AI models and MCP logic can continue processing data and making decisions without cloud connectivity, ensuring continuous operation and resilience.

10. How do I manage the APIs exposed by an MCP Gateway?

The APIs exposed by an MCP Gateway should be managed as part of your overall API lifecycle management strategy. This includes designing APIs using clear API design principles, versioning them, providing comprehensive documentation (perhaps via a local developer portal module on IoT Edge), and implementing API governance rules for consistency and security. Cloud-based API management platforms can also provide a centralized view and control over these edge APIs.