Model Context Protocol (MCP) is a standard interface that lets large language models (LLMs) communicate with external tools and data sources. It allows developers and tool providers to integrate once and interoperate with any MCP-compatible system.
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Connecting LLMs to external systems
LLMs don't have access to real-time or external data by default. To provide relevant context, such as current financial data, pricing, or user-specific data, developers must connect LLMs to external systems.
Each tool or service has its own API, schema, and authentication. Managing these differences becomes difficult and error-prone as the number of integrations grows.
Standardizing LLM interaction with MCP
MCP standardizes the way LLMs interact with tools and data sources. Developers implement a single integration with MCP, and use it to manage communication with any compatible service.
Tool and data providers only need to expose an MCP interface once. After that, their system can be accessed by any MCP-enabled application.
MCP is like the USB-C standard: instead of needing different connectors for every device, you use one port to handle many types of connections.
MCP servers, hosts and clients
MCP uses a client-server architecture for the AI model to external system communication. The user connects to the AI application, referred to as the MCP host, such as IDEs like Cursor, AI chat apps like ChatGPT or AI agents. To connect to external services, the host creates one connection, referred to as the MCP client, to one external service, referred to as the MCP server. Therefore, to connect to multiple MCP servers, one host needs to open and manage multiple MCP clients.
Build an MCP server with mcp-handler
Use mcp-handler to expose your application's tools, resources, and prompts to MCP clients. The package builds on the MCP TypeScript SDK and turns your server definition into a Web-standard HTTP request handler.
You can use mcp-handler with Next.js, Nuxt, SvelteKit, Hono, and other frameworks that support Web-standard Request and Response APIs. Follow the deployment guide to create a tool, test your MCP server, and deploy it on Vercel.
More resources
Learn more about Model Context Protocol and explore available MCP servers.
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Last updated September 15, 2026
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Cross-link map: Model Context Protocol (/docs/mcp)From the Vercel docs graph (built 2026-09-21T05:26:59.511Z), spanning vercel.com docs + KB, nextjs.org, ai-sdk.dev, and other Vercel documentation sites. Full graph as JSON: https://vercel.com/docs/graph.jsonSemantically closest pagesDeploy MCP servers to Vercel — Learn how to deploy Model Context Protocol \(MCP\) servers on Vercel with OAuth authentication and efficient scaling.MCP Integrations — Connect AI SDK, TanStack AI, and eve applications to MCP servers to discover and call tools.MCP server support on VercelIntroducing Vercel MCP: Connect Vercel to your AI toolsModel Context Protocol (MCP) explained: An FAQThis page links to (5)Tool CallingUse Vercel — Vercel MCP has tools available for searching docs, managing teams, projects, and deployments, and querying Web AnalyticsAI SDK and MCP — Connect an AI SDK app to an OAuth-protected MCP server with Vercel Connect, then handle user consent and tool approval.Deploy MCP servers to Vercel — Learn how to deploy Model Context Protocol \(MCP\) servers on Vercel with OAuth authentication and efficient scaling.MCP Integrations — Connect AI SDK, TanStack AI, and eve applications to MCP servers to discover and call tools.Pages that link here (12)By site: vercel-changelog (1) · vercel-kb (3) · vercel-web (2) · vercel-docs (6)From vercel-changelogOAuth support added to MCP AdapterFrom vercel-kbHow to create a contentful asset on Vercel — This is my wonderfulBuild a ChatGPT Connector \(MCP server\) — Build a ChatGPT MCP server with mcp-handler and Fluid compute. Add search, fetch, and OAuth, deploy to Vercel, then valiBuild an MCP Server with Weather tools using Express and Vercel — Make your Express weather API accessible to AI assistants through the Model Context Protocol.From vercel-webIntroducing Vercel MCP: Connect Vercel to your AI toolsThe second wave of MCP: Building for LLMs, not developersFrom vercel-docsMCP Integrations — Connect AI SDK, TanStack AI, and eve applications to MCP servers to discover and call tools.AI SDK with MCP — Connect the AI SDK to an MCP server on Vercel, discover its tools, and call them with models served through AI Gateway.eve with MCP — Give eve agents access to MCP tools through filesystem connections and authorize requests with Vercel Connect.TanStack AI with MCP — Connect TanStack AI to a Model Context Protocol server on Vercel and call its tools with models served through AI GatewaProducts — Browse Vercel products for building, deploying, securing, observing, and scaling web applications.Projects overview — A project is where you deploy and operate frontend apps, APIs, backends, containers, and agent workloads on Vercel.
