# What Is MCP? The Model Context Protocol, Explained for Founders > MCP connects AI apps to data, tools and prompts. How hosts, clients, servers, tools and transports fit together, and why founders should care. - Canonical: https://zeroraised.com/blog/what-is-model-context-protocol - Published: 2026-11-03T09:24:47Z - Author: ZeroRaised Team (https://zeroraised.com/blog/authors/zeroraised) - Publisher: ZeroRaised (https://zeroraised.com/) - Section: Growth - Tags: artificial-intelligence, developer-tools, distribution, saas The Model Context Protocol, or MCP, is an open standard for connecting an AI application to external systems: data sources, tools and workflows. Instead of building a bespoke connector for every assistant and every product, both sides implement one protocol ([MCP docs](https://modelcontextprotocol.io/docs/getting-started/intro)). For a bootstrapped founder, that changes the question from "which integration do we build next?" to "where does our product fit inside the AI client someone already uses?" **TL;DR** - MCP is an open-source standard for linking AI applications to external data, tools and workflows. - The architecture is host, client and server: one host creates one client for each server it connects to. - Servers expose tools, resources and prompts over a JSON-RPC 2.0 data layer. - Local servers usually use stdio; remote servers use Streamable HTTP and can serve many clients. - MCP gives small products a route into AI clients, but permissions and review are the price of entry. **Disclosure:** ZeroRaised publishes this analysis and is a launch site for bootstrapped products. Facts come from the linked sources as checked on 3 Nov 2026; ZeroRaised has no commercial relationship with the companies named. **Model Context Protocol (MCP)** is an open-source standard for connecting AI applications to external systems such as local files, databases, tools and specialised prompts ([MCP docs](https://modelcontextprotocol.io/docs/getting-started/intro)). ## What is the Model Context Protocol? The official introduction describes MCP as a way to connect AI apps such as Claude or ChatGPT to data sources (local files, databases), tools (search engines, calculators) and workflows (specialised prompts) ([MCP docs](https://modelcontextprotocol.io/docs/getting-started/intro)). The docs use a familiar analogy: "Think of MCP like a USB-C port for AI applications" ([MCP docs](https://modelcontextprotocol.io/docs/getting-started/intro)). The examples in the documentation are practical rather than abstract: agents accessing Google Calendar and Notion, Claude Code generating a web app from a Figma design, and enterprise chatbots connecting to multiple databases ([MCP docs](https://modelcontextprotocol.io/docs/getting-started/intro)). The stated benefits are less development time and complexity for developers, an ecosystem of tools for AI apps and more capable apps for users. The short version is "build once and integrate everywhere" ([MCP docs](https://modelcontextprotocol.io/docs/getting-started/intro)). ## How do hosts, clients and servers fit together? MCP uses a client-server architecture. An MCP host is an AI application such as Claude Code or Claude Desktop. The host creates one MCP client for each MCP server it connects to. The host coordinates the clients, each client keeps a connection to one server, and the server is the program that provides context ([MCP architecture docs](https://modelcontextprotocol.io/docs/learn/architecture)). The documentation gives a concrete picture: Visual Studio Code acting as a host connected to the Sentry MCP server remotely and to a local filesystem server, with one client object for each ([MCP architecture docs](https://modelcontextprotocol.io/docs/learn/architecture)). Adding a server adds a client, not a rewrite of the host. There are two layers. The data layer is the JSON-RPC 2.0 based protocol that handles discovery, tools, resources, prompts and notifications. The transport layer moves those messages ([MCP architecture docs](https://modelcontextprotocol.io/docs/learn/architecture)). Keeping them separate is why the same server logic can run locally or remotely. ## What are tools, resources and prompts? Servers expose three core primitives ([MCP architecture docs](https://modelcontextprotocol.io/docs/learn/architecture)): - **Tools** are executable functions, such as file operations, API calls or database queries. - **Resources** are data sources, such as file contents or database records. - **Prompts** are reusable templates, such as system prompts or few-shot examples. If you have built function calling into an AI feature, tools will feel familiar; the [function calling guide](https://zeroraised.com/blog/what-is-function-calling) covers that lower-level pattern. MCP adds discovery and a standard shape around it. One detail to note: sampling is deprecated as of protocol version 2026-07-28, and servers can elicit input from the user ([MCP architecture docs](https://modelcontextprotocol.io/docs/learn/architecture)). ## How do local and remote MCP servers differ? The transport decides where the server runs and who can reach it ([MCP architecture docs](https://modelcontextprotocol.io/docs/learn/architecture)). **stdio** is for local processes on the same machine. There is no network overhead, and local servers typically serve a single client. **Streamable HTTP** is for remote servers. It uses HTTP POST with optional Server-Sent Events, supports bearer tokens, API keys and custom headers, and typically serves many clients ([MCP architecture docs](https://modelcontextprotocol.io/docs/learn/architecture)). This is the transport that turns an MCP server into a product other people can connect to. The choice matters: a local server inherits the trust of its machine, while a remote server must handle authentication, tenancy and untrusted input. ## Why should founders care about MCP? MCP is a distribution surface. If your product is useful inside an AI client, an MCP server can put it there without asking every client to build a custom integration ([MCP docs](https://modelcontextprotocol.io/docs/getting-started/intro)). The [guide to building an MCP server](https://zeroraised.com/blog/how-to-build-an-mcp-server) covers the implementation, and the [MCP monetisation guide](https://zeroraised.com/blog/how-to-monetize-an-mcp-server) covers the business model question. The counterweight is competition. If every product can expose tools through the same protocol, the assistant can switch between them more easily. Your moat has to come from the quality of the result, the data you hold or the workflow you own, not from the connector itself. The [defensible AI product guide](https://zeroraised.com/blog/how-to-build-a-defensible-ai-product) goes deeper on that. ## What are the security basics? A protocol does not make a server trustworthy. Treat every MCP server as third-party code that can read data and take actions on your behalf. Practical rules, framed as advice rather than guarantees: 1. **Give least privilege.** Expose the smallest set of tools and data the job needs, not the whole account. 2. **Keep credentials separate.** Use scoped keys for each server rather than one master token. 3. **Log what happens.** Record tool calls and results so you can explain an action later. 4. **Review before connecting.** Read what a server does, who runs it and what it can reach. 5. **Keep a human on irreversible actions.** A delete, payment or send should not be one model step away from happening. These are operating habits, not protocol features. The responsibility for what you connect stays with you. ## What this means for bootstrapped founders MCP lowers the cost of being present in AI clients, which is the kind of distribution a small team can afford. A well-built server for a narrow job is a realistic project for one person. Do not confuse a connector with a business. Getting paid requires a reason for someone to choose yours, whether that is proprietary data, a better result or a workflow that saves real time. Start small. Pick one task your users already do inside an AI assistant, expose it as a clean tool, and measure whether people use it more than once. Decide on pricing only after you know the cost per task. The [LLM cost guide](https://zeroraised.com/blog/how-to-reduce-llm-costs) and the [AI pricing guide](https://zeroraised.com/blog/how-to-price-an-ai-product) are the next reads. Related reading from this week: [DeepSeek's Super-Agent Reads Your Team Chat. Proactive Coding Is Here](https://zeroraised.com/blog/deepseek-super-agent-team-chat-coding) and [The NYT Sued Brave. Founders Building on Web Data, Read This](https://zeroraised.com/blog/nyt-brave-lawsuit-ai-search-api-risk). ## Where ZeroRaised fits ZeroRaised is a launch and discovery site for products built without institutional venture capital or accelerator equity. If you have an MCP server or any product people can try, you can [submit it here](https://zeroraised.com/submit). The [pricing page](https://zeroraised.com/pricing) explains the current options. For more practical analysis, [join the ZeroRaised newsletter](https://zeroraised.com/newsletter). No promises of traffic or results, just the useful parts. ## Sources - [MCP docs: What is the Model Context Protocol?](https://modelcontextprotocol.io/docs/getting-started/intro) - [MCP docs: Architecture overview](https://modelcontextprotocol.io/docs/learn/architecture) ## Frequently asked questions ### What is MCP in simple terms? MCP is a common way for an AI application to plug into outside systems. Instead of every AI client building a custom integration for every tool or database, both sides speak the same protocol. The official docs compare it to a USB-C port for AI applications. ### Is MCP the same as an API? No. An API is an interface a service exposes to other software. MCP is a standard that an AI application uses to discover and call tools, read resources and load prompts from a server. An MCP server often wraps an existing API, but the protocol also covers local files and other context. ### What are the three types of MCP server primitives? The architecture docs list tools, which are executable functions such as file operations or database queries; resources, which are data sources such as file contents or database records; and prompts, which are reusable templates such as system prompts or few-shot examples. ### Do MCP servers run locally or remotely? Both. Local servers typically use the stdio transport and run as processes on the same machine, usually serving a single client. Remote servers use Streamable HTTP, can serve many clients and support bearer tokens, API keys and custom headers. ### Is MCP secure by default? No protocol can make a third-party server safe on its own. Treat every server as code with access to your data and tools. Give it the least privilege it needs, keep credentials separate, log what it does and review a server before you connect it to anything important.