Declarative Agents Meet MCP Servers and Rich UI Widgets: Building the Next Generation of Interactive AI Experiences

In the evolving landscape of AI agents, declarative agents represent a significant shift toward high-level, intent-driven orchestration.

Rather than micromanaging every step through imperative code, developers or even power users define what the agent should achieve—including goals, capabilities, data sources, and behaviors—while the underlying runtime and large language model handle the how.

This approach, popularized by platforms like Microsoft 365 Copilot, allows agents to be authored primarily through configuration files, manifests, and natural language instructions.

This declarative foundation proves powerful yet was initially limited to text-based responses. The integration of Model Context Protocol (MCP) servers and MCP Apps, which deliver rich interactive UI widgets, dramatically expands these capabilities. Together they enable agents that deliver dynamic, visual, and highly actionable experiences directly within the chat interface.

Understanding Declarative Agents

Declarative agents, particularly in the Microsoft 365 ecosystem, are defined through manifests that specify instructions and persona, available tools and actions, knowledge sources for grounding, and orchestration logic. Unlike fully custom-coded agents, much of the heavy lifting—such as reasoning, planning, tool selection, and conversation management—is handled by the host platform. This design makes declarative agents faster to build, easier to maintain, and more consistent across users.

The main limitation of earlier versions was that traditional tool calls returned structured data, which the agent then summarized into plain text. For complex workflows involving dashboards, multi-step forms, data visualizations, or interactive task managers, this text-only approach often felt insufficient.

The Role of the Model Context Protocol (MCP)

The Model Context Protocol (MCP), originally introduced by Anthropic and now widely adopted, serves as a standardized bridge connecting LLMs and agents to external tools, data sources, and additional capabilities.

An MCP server functions as a smart adapter that exposes tools, resources, and prompts in a standardized, discoverable manner that agents can consume securely. It supports flexible transports, running locally via stdio or remotely over Streamable HTTP, and handles authentication along with business logic.

MCP Apps: From Data to Rich Interactive Interfaces

The real breakthrough arrives with MCP Apps, an extension to the MCP protocol that allows servers to deliver not only data but also full interactive user interfaces rendered directly inside the host environment, such as Copilot Chat.

These MCP Apps transform tool responses into data visualizations like charts and dashboards, interactive forms and multi-step wizards, task management interfaces such as kanban boards or approval flows, and custom components built with modern frameworks.

How the Architecture Works

Architecturally, the process works as follows. The declarative agent includes an MCP server action within its manifest or configuration. When the agent decides to invoke a relevant tool, the MCP server can respond with structured content that includes UI definitions, often based on React or declarative UI specifications.

The host platform then renders the widget inline within the chat while preserving full conversation context. User interactions with the widget trigger callbacks back to the MCP server or the agent, enabling further orchestration and real-time updates. This creates a seamless blend of conversational AI and application-like experiences.

Building Powerful Declarative Agents with MCP

To build such a powerful declarative agent, developers begin by defining the agent’s core instructions and persona using tools like the Microsoft 365 Agents Toolkit.

For example, an agent might be instructed to act as a project manager capable of querying Jira, visualizing timelines, and managing approvals. Next, they build or connect an MCP server that implements the necessary tools, incorporating proper authentication mechanisms such as Entra ID or OAuth for enterprise security.

Tools are then extended with MCP Apps by including UI payloads in their responses, leveraging available samples and SDKs for rapid development. Finally, the solution is tested for tool discovery and UI rendering, with generative orchestration ensuring the agent intelligently chooses when to surface rich widgets versus simple text responses.

A Typical User Experience Flow

Consider a typical user flow. A user asks to see the status of a Q2 marketing campaign. The agent invokes the appropriate MCP tool, and the server returns both the underlying data and a dashboard widget. The user can then interact directly with the widget—applying filters, drilling down into details, or triggering actions such as budget approval—all while remaining within the same cohesive chat experience.

Key Benefits and Real-World Use Cases

This combination delivers substantial benefits. It greatly enhances user experience by moving beyond chat as a mere wrapper to true interactive applications, where users gain rich visuals and direct manipulation without context switching. Developer productivity rises because declarative definitions pair with reusable MCP servers, allowing composition from modular building blocks. Enterprise readiness is supported through secure authentication, governance, and composability, as MCP servers can be shared across multiple agents and organizations.

Practical use cases span HR and finance applications with interactive org charts and expense approvals, project management with real-time Gantt charts or kanban boards, customer support with dynamic troubleshooting wizards, and analytics featuring ad-hoc dashboards that evolve through conversation.

Challenges and Best Practices

Several challenges require attention during implementation. Security remains paramount, demanding input validation on the MCP server and proper scoping of permissions. Performance considerations suggest keeping widgets lightweight and offloading heavy computation to the server side. Tool discoverability should be optimized with clear descriptions so the agent reliably selects the right capabilities, including those with UI support. Developers should also implement graceful fallbacks to text when UI rendering is unavailable and maintain careful versioning between manifests and server implementations.

Looking Ahead: The Future of Agentic Applications

Combining declarative agents with MCP servers and rich UI widgets points toward a future where AI transcends the role of conversational sidekick to become the interface itself. Agents evolve into dynamic applications that adapt their user interface to the specific task and user context. Standardized protocols foster an ecosystem of interoperable tools and components. As major platforms deepen their support for MCP, the barrier to creating sophisticated, interactive AI experiences drops dramatically. Developers can focus more on domain logic and delightful interfaces rather than low-level plumbing.

The declarative agent extended with MCP therefore represents not the end of the journey, but the solid foundation for truly agentic applications that feel both powerful and deeply human.