🤖 Ghostwritten by GPT 5.4 · Fact-checked & edited by Claude Opus 4.6 · Curated by Tom Hundley
MCP Apps change the developer experience for agentic systems in one important way: tools no longer have to end at plain text. With MCP Apps, a tool can return structured, interactive UI directly inside the conversation, which means your agent can render dashboards, forms, approval steps, visualizations, and multi-step workflows without forcing users to context-switch into a separate web app. For developers, that turns Model Context Protocol UI from an interesting concept into a practical delivery surface.
The immediate takeaway is simple: if you already have MCP tools, you can start upgrading the highest-friction ones this week. Begin with tools that currently dump large JSON payloads, require follow-up clarification, or hand users off to another interface. Those are strong candidates for MCP Apps because agentic UI components reduce ambiguity, improve task completion, and create a tighter feedback loop between model reasoning and user action.
This article focuses on implementation: where MCP Apps fit in the protocol stack, how to shape tool responses, how to build safe interactive AI tools, and what production teams should watch before rolling Model Context Protocol UI into real workflows.
TL;DR: MCP Apps make tools more usable by letting MCP servers return UI affordances instead of forcing everything through text and raw JSON.
If you have built an MCP server already, you know the common failure mode: the model calls a tool successfully, but the result is awkward for humans. A dataset comes back as a wall of rows. A workflow requires six clarification turns. An approval process needs a button, not another paragraph. MCP Apps target exactly that gap.
The technical significance is bigger than “pretty output.” MCP Apps create a standard path for interactive rendering inside MCP clients. That means developers can design tools that return:
In practical terms, this shifts tool design from “what string should this tool return?” to “what state, schema, and actions should this tool expose?” That is a better abstraction for real business workflows.
For teams still getting oriented, ESS recommends reviewing What is MCP? The Model Context Protocol Explained before implementing UI-returning tools, and pairing it with MCP Architecture: How the Model Context Protocol Works if you need a refresher on client-server responsibilities.
A useful benchmark comes from the broader UI ecosystem: according to the Stack Overflow Developer Survey 2024, JavaScript and TypeScript remain among the most commonly used languages by professional developers. That matters here because early MCP Apps work will likely land in TypeScript-heavy stacks where schema validation, server-side tool execution, and web UI rendering are already standard practice. Likewise, React remained one of the most commonly used web frameworks in that survey, which makes component-based rendering a natural fit for interactive AI tools.
The definitive statement is this: MCP Apps are the missing presentation layer for production-grade tool calling. Tool invocation solved action. MCP Apps start solving interaction.
Think of MCP Apps as an extension to the result contract between tool and client. The model still plans, selects a tool, and executes it through the client-server MCP boundary. The difference is that the tool can now return both machine-usable data and client-renderable UI metadata.
That separation is important because it preserves core protocol benefits:
TL;DR: The safest way to build MCP Apps is to return structured data, a declarative UI description, and explicit follow-up actions rather than mixing business logic into the front end.
The implementation pattern most teams should adopt is a three-part contract:
Even if specific SDK ergonomics evolve, that pattern is stable. Keep your server authoritative and your UI declarative.
Below is a sanitized TypeScript example showing the pattern. This is not tied to private ESS systems and uses placeholder structures to illustrate MCP extension development.
import { z } from "zod";
const TicketQuery = z.object({
team: z.string(),
status: z.enum(["open", "closed", "stale"]).optional(),
limit: z.number().int().min(1).max(100).default(20)
});
type TicketQuery = z.infer<typeof TicketQuery>;
export async function getSupportDashboard(rawInput: unknown) {
const input = TicketQuery.parse(rawInput);
const rows = await fetchDashboardData(input); // your internal service layer
return {
content: [
{
type: "text",
text: `Loaded ${rows.length} support tickets for ${input.team}.`
}
],
structuredContent: {
team: input.team,
status: input.status ?? "all",
rows,
generatedAt: new Date().toISOString()
},
app: {
type: "dashboard",
version: "1.0",
layout: {
sections: [
{
kind: "summary-cards",
items: [
{ key: "total", label: "Total Tickets", value: rows.length },
{ key: "stale", label: "Stale", value: rows.filter(r => r.isStale).length }
]
},
{
kind: "table",
dataKey: "rows",
columns: [
{ key: "id", title: "ID" },
{ key: "priority", title: "Priority" },
{ key: "owner", title: "Owner" },
{ key: "updatedAt", title: "Updated" }
]
}
]
},
actions: [
{
id: "refresh",
tool: "getSupportDashboard",
input: input
},
{
id: "assignTicket",
tool: "assignTicket",
selectionRequired: true
}
]
}
};
}The pattern matters more than the exact property names. Your tool should return a stable data object, then a renderable app description that the client can safely interpret.
Avoid shipping arbitrary front-end logic inside tool responses. A declarative app payload gives you:
This is the same architectural instinct behind well-designed API-first systems: the server declares intent, the client chooses how to present it.
According to the State of JS 2024 results, developers continue to favor typed and component-driven front-end workflows. That trend reinforces why schema-first UI contracts are likely to win over ad hoc HTML or opaque blobs in Model Context Protocol UI implementations.
For most teams building early MCP Apps, a productive stack looks like this:
| Layer | Recommended approach | Why it works |
|---|---|---|
| MCP server | TypeScript on Node.js 20+ | Fast iteration, strong SDK ecosystem |
| Input validation | Zod | Runtime validation plus inferred types |
| UI schema | JSON-serializable declarative components | Portable across clients |
| State store | Server-owned session or workflow state | Prevents UI tampering from becoming business logic |
| Rendering layer | React component registry in the client | Maps schema to trusted components |
| Observability | OpenTelemetry + structured logs | Tracks tool calls and UI action loops |
If your team is evaluating whether to build a custom stack or wait for framework maturity, our article DIY Agent Fleet vs 2026 Frameworks provides a useful lens for deciding where standardization helps and where custom control still matters.
TL;DR: Treat MCP Apps as untrusted input at the rendering boundary, and keep authorization, validation, and side effects on the server.
As soon as tools can render dashboards and forms, developers are tempted to move too much logic into the UI payload. Do not do that. The UI is an interaction surface, not an authority surface.
The clean security model is:
That rule matters even more for multi-step workflows like approvals, edits, provisioning, or data exports.
The most common mistakes in early MCP extension development will look familiar to anyone who has secured workflow software before.
Anti-pattern 1: trusting client-submitted derived fields
If a form posts back total cost, priority score, or policy classification, recompute it on the server.
Anti-pattern 2: embedding executable code in tool results
Do not let a tool return arbitrary scripts or uncontrolled markup. Return declarative component definitions and bind them to vetted client components.
Anti-pattern 3: confusing selection with authorization
A user selecting a row in a table does not mean they are allowed to modify that record. Every follow-up action still needs policy checks.
Anti-pattern 4: hiding side effects behind “UI actions”
Every action button should map to an explicit tool call or server mutation with logging, policy enforcement, and traceability.
const AssignTicketInput = z.object({
ticketId: z.string().uuid(),
assigneeId: z.string().uuid(),
actorId: z.string()
});
export async function assignTicket(rawInput: unknown, context: RequestContext) {
const input = AssignTicketInput.parse(rawInput);
await requirePermission(context.user, "ticket.assign", input.ticketId);
const ticket = await loadTicket(input.ticketId);
if (!ticket) throw new Error("Ticket not found");
const updated = await updateTicketAssignment({
ticketId: input.ticketId,
assigneeId: input.assigneeId,
changedBy: context.user.id
});
await auditLog({
action: "ticket.assign",
actorId: context.user.id,
resourceId: input.ticketId
});
return {
content: [{ type: "text", text: "Ticket assignment updated." }],
structuredContent: updated
};
}That pattern keeps the app interactive while preserving enterprise-grade controls. For deeper hardening guidance, link your implementation review with MCP Security: Best Practices for Production Deployments and, for policy-oriented threat modeling, MCP Policy Controls: Defending Against Agent-in-the-Middle Attacks After CVE-2026-21536.
The definitive statement here is: interactive output does not reduce the need for API discipline; it increases it.
TL;DR: Production MCP Apps succeed when UI state is minimal, tool round-trips are explicit, and every user interaction is traceable as part of the agent workflow.
The hard part of interactive AI tools is rarely the first demo. It is keeping the action loop coherent under retries, partial completions, human edits, and model re-planning.
In practice, you have three options:
| State strategy | Best for | Trade-offs |
|---|---|---|
| Stateless app responses | Read-only dashboards, one-shot forms | Simple, but weak for multi-step flows |
| Server session state | Guided workflows and approvals | Easier continuity, but requires expiration and cleanup |
| Durable workflow state | Long-running operations and human-in-the-loop tasks | Most robust, but operationally heavier |
For most teams starting with MCP Apps, server session state is the right middle ground. Persist only the workflow state you need, not the entire rendered UI. UI should be reproducible from state plus schema.
An interactive card inside a conversation feels broken much faster than a traditional app page. Users tolerate slower text generation more than sluggish button responses. That means you should optimize:
Google’s Core Web Vitals guidance has trained developers to think in responsiveness terms, and that instinct applies here too even though the surface is conversational rather than page-based. Responsive interaction loops matter because they shape trust in the agent.
Log these events separately:
That instrumentation helps answer operational questions your product team will ask immediately:
According to the CNCF Annual Survey 2023, observability remains a core operational priority across cloud-native teams. The same principle applies here: if you cannot trace your agentic UI components end to end, you do not yet have a production system.
TL;DR: Start with tools that already have clear structure and repeated user interaction patterns: dashboards, review queues, approval forms, and remediation workflows.
Not every MCP tool needs a UI. The best early candidates are tools where text is currently compensating for a missing interface.
1. Review queues
Security findings, support tickets, invoice exceptions, and QA triage all map well to selectable tables plus constrained actions.
2. Approval flows
Budget approvals, change management, access requests, and exception handling benefit from structured forms and explicit confirmation states.
3. Operational dashboards
Incident summaries, pipeline health, and backlog views are easier to act on when users can sort, filter, and drill into a concise surface.
4. Guided remediation
If your current tool output ends with “Would you like me to fix that?”, MCP Apps let you present the exact next actions with clear context.
This is the build list most teams can execute this week:
If your developers need to ramp up fast, start with Build Your First MCP Server: Step-by-Step Tutorial and then layer UI-returning patterns on top of the same tool boundary. The underlying discipline does not change; the presentation contract becomes richer.
The biggest mindset shift is this: MCP Apps are not a replacement for good tools. They are a multiplier for well-designed tools. If the tool contract is vague, adding UI will only hide the problem temporarily.
MCP Apps are an MCP extension pattern that allows tools to return interactive UI components directly inside supported AI conversations. Instead of returning only text or raw structured data, a tool can provide a renderable app surface such as a dashboard, form, visualization, or guided workflow. The server still owns logic and state; the client renders trusted components from the returned schema.
Ordinary tool calling typically ends with text plus maybe some structured output for the model to interpret. MCP Apps add a presentation layer so the client can render interactive AI tools for the user. That makes follow-up actions more explicit, reduces conversational friction, and supports human-in-the-loop workflows more naturally.
Start with one tool that already returns structured data and currently creates friction in the chat experience. Good examples include approval queues, incident dashboards, ticket triage, and reporting tools with filterable results. Keep the first version small: one read view, one action, server-side validation, and full tracing.
They can be, but only if you treat the UI payload as untrusted at the execution boundary. Keep authorization, validation, and side effects on the server, and map returned schemas to vetted client components rather than rendering arbitrary code. Production readiness depends more on policy, observability, and action design than on the UI itself.
No. MCP Apps are best viewed as a conversational interaction layer for workflows that benefit from model guidance and in-thread action. Many teams will use them alongside existing internal apps, not instead of them, especially when full-screen analysis or complex administration still belongs in a dedicated product surface.
MCP Apps make Model Context Protocol UI materially more useful for developers because they turn tool calls into usable workflow surfaces, not just text responses with better formatting. That is a meaningful step toward interactive AI tools that can support real operational work inside the conversation loop.
For engineering teams, the path forward is clear: pick one structured tool, define a safe UI contract, and ship an end-to-end action loop with tracing and policy enforcement. If your team wants help designing production-ready MCP Apps, Elegant Software Solutions can help through our AI Implementation services, from MCP architecture to secure agentic workflow delivery. Schedule a working session at https://www.elegantsoftwaresolutions.com/schedule.
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