Similar to how HTTP defined internet communication standards in the early 1990s, we now see the emergence of a new layer of interoperability, the Model Context Protocol (MCP), a standard that enables large language models (LLMs) and AI assistants to interact consistently with external tools, data sources, and services.

Yet, as with past technological booms, many businesses without a genuine AI foundation label themselves as “AI companies” to ride the wave of enthusiasm. History doesn’t repeat exactly, but it rhymes: during the Dot-Com Bubble of 1999, adding ‘.com’ to a company’s name inflated valuations overnight (Investopedia). In the Gen-AI Boom of 2024-25, calling a product “AI-powered” plays a similar role — attracting rapid funding and speculative attention.

The reality check is that while the internet’s transformative potential was real, enduring value accrued primarily to robust platforms. The AI landscape today similarly favors substantial companies over single-feature startups.

In the sections ahead, we define what MCP is (and isn’t), compare it with HTTP, answer whether it replaces HTTP, spotlight a creator use case for AI search, map where your business fits with an Executive Cheat Sheet, and close with the bottom line.

HTTP in the ’90s → MCP in the mid-2020s

  • A streamlined, universal connector. Before HTTP, serving a single document online required separate FTP, Gopher, and CGI setups, each with its own client software and link syntax (Berners-Lee, 1989). HTTP consolidated this into one protocol and address scheme, allowing universal browser access. MCP targets a similar pain point for AI: today, every “AI integration” has its own REST schema and JSON structures. MCP replaces that complexity with a single JSON-RPC dialect plus a capability handshake. An LLM client can list a server’s tools (actions), resources (data), and prompts (expert instructions) at runtime, then call them without custom wrappers. Supported transports include stdio, WebSocket, or plain HTTP, making MCP compatible across various environments (MCP Wikipedia). Early adopters include Anthropic, OpenAI, Google’s Toolbox for Databases, VS Code, and Replit.

  • Network effects outweigh isolated features. HTTP’s ubiquity created a flywheel effect: each new web page made the protocol more valuable, encouraging further adoption. MCP can spark the same loop—additional hosts or servers enhance the standard’s value, motivating broader adoption (Reddit discussion).

  • Infrastructure endures, applications evolve. While early web browsers rose and fell, infrastructure providers like Cisco, Akamai, and AWS thrived by managing traffic and scalability. Similarly, MCP’s lasting value will accrue to infrastructure providers and holders of critical datasets essential for LLMs.

MCP vs. HTTP

MCP serves as an AI-focused alternative to integration standards like SOAP, RAML, and OpenAPI. SOAP offers messaging, RAML and OpenAPI provide interface definitions; MCP integrates both transport and capability discovery.

Basic HTTP endpoints often require additional tooling like OpenAPI or custom documentation for AI integration, leading to errors and inefficiencies. MCP simplifies complexity into three fundamental primitives:

  • Tools – perform actions (akin to POST/PUT)

  • Resources – fetch information (akin to GET)

  • Prompts – expert guidance

Each primitive has structured descriptions acting as built-in documentation, enhancing usability.

Conceptually, MCP complements other interfaces:

  • Frontend → Humans

  • REST API → Traditional apps/services

  • MCP API → AI agents

While humans can technically use REST APIs and apps can access frontends, dedicated interfaces optimize usability.

Anthropic’s USB-C analogy clearly illustrates MCP’s purpose. Before USB standardization, connecting devices involved complex setups. USB simplified interactions, and USB-C further enhanced usability by addressing connector orientation, speed, and power issues (Anthropic’s MCP Intro).

Traditional protocols like gRPC, GraphQL, and OpenAPI focus on deterministic clients - they define static interfaces for pre-coded systems. MCP, by contrast, introduces semantic interoperability for probabilistic agents: it lets models dynamically discover available tools, data, and playbooks at runtime. Instead of hard-coded API schemas, it provides self-describing capability layers that LLMs can query, reason about, and invoke safely. In short, where OpenAPI documents how to call a system, MCP lets the system describe itself to an AI.

Will MCP Replace HTTP?

Aspect HTTP Strengths MCP Strengths
Reach Ubiquitous, browser-supported AI-focused, precise interactions
Tooling needs External schemas/documentation Built-in discovery and documentation
Best suited for Web interfaces, static content AI workflows, context management

HTTP remains essential for browsers, caches, and CDNs. MCP complements HTTP by optimizing deterministic interactions and AI-first semantics.

Remark: MCP gives clients predictable call semantics and audit-friendly error handling by standardizing the envelope (JSON-RPC, tool/error schemas). Truly deterministic results—returning the exact same bytes for the same request — require a policy layer above the core protocol (e.g., content-addressed URIs or versioned snapshots).

Creators: AI Search via MCP

Google’s recent shift toward AI-generated answers, rather than simply linking to external websites, has sparked concern among content creators about the future of the web’s funding model. However, rather than viewing this as a potential apocalypse for online publishers, MCP offers a compelling opportunity for reinvention.

Instead of publishing HTML pages for search engines to crawl, content creators could expose structured Resources (data) and curated Prompts (expert interpretations) directly through standardized MCP servers. Search engines, including Google’s new AI-enhanced search tools, could then access and present verified, authoritative content, clearly crediting original providers and potentially compensating them on a per-query basis. In this new paradigm, content creators become valued Context Providers, benefiting directly from deterministic interaction logs and fair, usage-based revenue streams, independent of fluctuating traffic or ad revenue.

Executive Cheat Sheet: Where MCP Fits in the AI Stack

Use this to quickly place your business or product and see the landscape clearly. MCP is an interface—not a business model. It connects AI models to data and workflows so systems become more interoperable and reliable. Some players act mainly as MCP servers (expose data/actions); others as clients (call them); many are both.

AI Layer What they actually do Honest label MCP interface (typical) Durability driver
Model Labs Build/serve foundation models AI company Client to enterprise Tools/Resources Model IP + research velocity
Infra Platforms Compute, data, orchestration Infra-AI Both (host servers; orchestrate clients) Distribution + ecosystem lock-in
Domain SaaS (vertical apps) Ship workflows with embedded AI AI-enabled software Both (Tools/Resources + Playbook Prompts*) Workflow ownership, switching costs
Context Providers (data/content) Provide proprietary datasets/POV Context provider Server (Resources/Prompts) Unique data, credibility, refresh rate
AI-washing Superficial “AI” add-ons Hype-risk None (thin—nothing to standardize) Fragile without real capability

Playbook Prompts: prompts that encode your standard operating procedures so the AI follows your steps consistently.

Heuristic: If you own workflows, expose them as Tools. If you own data/context, expose them as Resources/Prompts. If you build models/agents, be a strong Client. If your strategy is “add MCP”, first decide what you own.

Bottom Line

What MCP is. The interface layer that lets AI agents operate real systems — standardizing Tools (actions), Resources (data), and Prompts (expert playbooks). Think HTTP for AI operations.

What it isn’t. Not a replacement for HTTP; it complements it with discovery, auditability, and interoperability for AI workflows.

Why now. AI is moving from chat to execution. Without a standard, every integration is brittle glue code with weak governance. MCP brings predictable calls, capability discovery, and traceable outcomes.

Thesis in one line. MCP turns fragmented AI experiments into integrated, governable workflows — favoring teams that own data or workflows and ship reliable interfaces over those selling hype.

Winners will own valuable assets (data, infrastructure, trust) — not just labels. Decide whether you are a model lab, infrastructure provider, or context provider; otherwise, you’re likely their customer.