Skybridge MCP Apps Framework
Build type-safe MCP applications for Claude, ChatGPT, and other compatible clients with TypeScript and React.
CI jobs generally use contents:read, generated view resources declare explicit CSP domains, installs use a frozen lockfile, and the manifest contains several transitive dependency security overrides; this supports moderate least-privilege and dependency-security scores. The README identifies server-model-UI data-flow topics, while tests expose tool input/result, model-context, download, and metadata channels, but the supplied files provide no complete policy for data classification, retention, telemetry, or credential handling. Applications may add connection/resource domains and view._meta may override framework-computed security metadata, so the boundary is configurable rather than strictly enforced. No user-confirmation mechanism before consequential tool calls is shown. External effects are application-defined, with examples involving booking, carts, OAuth, downloads, and model-context updates, but no uniform confirmation or side-effect annotation standard appears. Content hashes help isolate versions, yet no action undo or deployment rollback procedure is documented. The MIT file, author, named maintainers, organization, and security address provide attribution, but publisher identity remains unverified by the stated registry and customer-logo claims are not independently supported by the supplied material.
The README's type-safe MCP tools, React views, and multi-host claims align closely with the schemas, resource metadata, CSP construction, and CI/conformance workflows, earning strong self-consistency. Explicit Node and pnpm versions, frozen installs, Windows builds, and broad CI checks improve dependency availability; deductions remain because Notte, Slack, MCP hosts, and external hosting services are operational or verification dependencies without documented offline or degraded-mode guarantees. Tests demonstrate a specific duplicate-view exception, large-output warnings, failure artifacts, and notifications. The score is reduced because a missing production manifest silently falls back to an unversioned URI and the evidence does not show consistently actionable messages for all runtime failures.
The material explicitly serves both humans and coding agents and covers creation, migration, new views, commerce, travel, SaaS, authentication, and interactive UI scenarios, providing thorough audience coverage. Tool names and descriptions, Zod input/output schemas, optional views, host capabilities, and the one-tool-per-view rule provide useful capability boundaries and trigger signals. They are not complete enough for full scores because no repository-level agent manifest, unified trigger policy, or side-effect classification is shown. Environment fit is strongly supported by stated Claude, ChatGPT, VS Code, and compatible-client targets, Linux and Windows CI, example and documentation builds, a ChatGPT/Claude/Alpic conformance matrix, and self-hosting on Node-compatible platforms.
The README has clear sections for purpose, features, agent and human setup, documentation, deployment, community, maintainers, and categorized examples, supporting strong information architecture and examples. Installation commands are directly usable, but prerequisites and the full production procedure are deferred to external documentation not included here. Tests acknowledge a deprecated hosts option, and release branches plus a bump script suggest some migration discipline; however, the monorepo root is still version 0.0.0 and no changelog or stability policy is supplied. Known limitations are scattered through tests, such as one tool per view and manifest fallback, rather than collected for users. LICENSE and README say MIT while the root package.json says ISC, a material metadata contradiction that prevents a strong license score. Named maintainers, the team, issue and contribution channels, and a private security address clarify responsibility, but there are no response-time or support commitments and publisher identity is not registry-verified.
Structured content, text content, metadata, React views, CSP, development HMR, production asset hashing, and numerous examples make framework outputs directly usable for MCP apps, supporting full output-usability credit. Host abstraction, end-to-end type inference, and a local emulator plausibly add value over low-level SDKs and are partly corroborated by tests; deductions remain because claims such as seamless portability, the permanent tunnel, and the complete development experience are not fully evidenced in the supplied implementation excerpts. MIT licensing and self-hosting reduce adoption costs, while Node >=24.18, pnpm, browser testing, cloud conformance, authentication providers, and optional Alpic deployment add environment and operational costs. No performance or resource-cost measurements are supplied.
Many core claims trace to concrete tests and workflows: schema-based registration, resource URIs, content normalization, CSP, version hashing, host matrices, and cross-platform builds. README, test code, and workflow configuration provide useful cross-source corroboration, but this remains a static review without execution results, the lockfile, complete core implementation, or release history. Promotional assertions such as “seamlessly,” “delightful,” and “permanent tunnel,” along with customer-logo adoption claims, are not clearly separated from audited facts or supported inference, limiting fact/inference separation.
- The root package.json declares ISC while LICENSE, README, and the supplied license metadata declare MIT; determine the effective license and align metadata before release or redistribution.
- No uniform user-confirmation, undo, or side-effect annotation mechanism is shown for consequential tools; applications should add explicit confirmation around bookings, purchases, file operations, authentication, and external API writes.
- view._meta can override framework-computed metadata and applications can extend CSP domains; review each application's connectDomains, resourceDomains, and overrides to avoid broadening data exposure.
- The permanent tunnel, seamless host portability, customer adoption, and cloud conformance outcomes were not independently verified by this static evidence set.
- Node >=24.18 and several cloud, host, and authentication integrations may constrain deployment compatibility; validate the target environment and service dependencies before adoption.
What does this agent do, and when should you use it?
Skybridge is a full-stack, React-first TypeScript framework for MCP Apps and MCP Servers, rather than a standalone general-purpose AI assistant. It connects MCP server tool definitions, model interactions, and React views with tRPC-style type inference and React Query-style hooks. Its development environment includes a local emulator, hot module reload, and a permanent tunnel for connecting a local application to Claude and ChatGPT. The framework abstracts implementation differences across MCP clients, with documented targets including Claude, ChatGPT, VSCode, and other compatible clients. Finished applications can be deployed through Alpic or self-hosted on any Node.js-compatible platform, and the repository includes examples spanning commerce, travel, productivity, authentication, and generative UI.
A developer scaffolds a project with npm create skybridge@latest my-app, or equips a coding agent with the Skybridge skill by running npx skills add alpic-ai/skybridge -s skybridge. The application defines tools in an MCP server and connects their data to a React view; Skybridge supplies type inference from server tool definitions to the frontend and React Query-style hooks for interaction and state. Its development server runs a local emulator, hot module reload, and a permanent tunnel that connects the local application to Claude and ChatGPT. The framework coordinates data flow and context synchronization among the server, model, and UI, producing an interactive MCP application together with its MCP server. Production delivery can use Alpic or a self-hosted Node.js-compatible environment.
- A TypeScript team building maps, carousels, charts, forms, or other interactive views inside ChatGPT or Claude can implement the MCP server and React UI in one framework.
- A developer with an existing low-level MCP server can migrate toward typed tool-to-view contracts, hooks, hot reload, and a local emulator.
- A product team targeting Claude, ChatGPT, VSCode, and other compatible MCP Apps clients can reduce duplicated client-specific implementation.
- An engineering team can install the supplied skill so a coding agent can create an MCP app, migrate an MCP server, or add a view.
- A team implementing personalized OAuth flows can study the Descope, Clerk, WorkOS AuthKit, Stytch, Auth0, and Authplane examples.
- A developer validating a commerce, flight-search, analytics-dashboard, interactive-tutoring, or generative-UI prototype can start from the corresponding example.
What are this agent's strengths and limitations?
- tRPC-style inference carries types from MCP server tool definitions into React views, providing a concrete end-to-end contract.
- The integrated local emulator, hot module reload, and permanent tunnel cover the documented local iteration path for Claude and ChatGPT.
- A shared abstraction targets Claude, ChatGPT, VSCode, and other compatible MCP Apps clients, reducing repeated client-specific work.
- React Query-style hooks, a CLI, an agent skill, and programmatic development-tool APIs support both human and coding-agent workflows.
- Deployment is not limited to the vendor platform: Alpic hosting and self-hosting on Node.js-compatible platforms are both documented.
- The central UI capability depends on clients that support MCP Apps or other UI-enabled MCP behavior; non-UI MCP environments cannot use that feature directly.
- Self-hosting requires a Node.js-compatible platform, while the supplied material gives no minimum Node.js version, production topology, or resource requirements.
- The permanent tunnel and documented Claude/ChatGPT connection workflow require network access; offline development behavior is not described.
- The abstraction exists because MCP client implementations differ, but no feature-by-feature compatibility matrix or parity limits are supplied.
- Alpic adds hosting, analytics, compliance auditing, and submission assistance, but its pricing, account requirements, and authentication procedure are not documented here.
How do you install or deploy this agent?
You need shell access, network access, a writable project directory, npm/npx, and a Node.js-compatible runtime. The supplied material does not state a minimum Node.js version or enumerate accounts and credentials required by individual examples. Scaffold a project with npm create skybridge@latest my-app. For coding-agent workflows, install the skill with npx skills add alpic-ai/skybridge -s skybridge. After skill installation, ask the agent “What skills do you have?” to confirm that it is available.
How do you use this agent?
Run npm create skybridge@latest my-app, then define tools in the generated MCP server and connect them to React views and framework hooks. Iterate through the development server's local emulator, hot module reload, and permanent tunnel, connecting the local application to Claude or ChatGPT. The supplied material does not provide the exact development-server start command, so no command name can be stated reliably. For working patterns, use examples such as Everything, Capitals Explorer, Flight Booking, Ecommerce Carousel, Productivity, Manifest UI, or Generative UI. Deploy the result on Alpic or follow the deployment path for a self-hosted Node.js-compatible platform; the supplied material does not specify Alpic authentication, account setup, or deployment CLI commands.
How does this agent compare with similar options?
Compared with raw MCP SDKs, Skybridge adds hooks, end-to-end type safety, hot module reload, a local emulator, permanent tunneling, and a cross-client abstraction. Adopting it also introduces Skybridge conventions, a React-oriented frontend model, and a Node.js runtime boundary. No direct comparison with another named MCP framework is provided.