What is an MCP server? The USB port of AI assistants, explained

By Melvin Tercan, Octagon Research Editor

An MCP server is a service that gives AI assistants structured access to tools and live data through the Model Context Protocol — an open standard introduced by Anthropic in late 2024 and adopted across the industry through 2025. Instead of every app building custom integrations with every data source, an MCP server describes its tools once, and MCP-capable clients — Claude, Cursor, Windsurf, agent frameworks — can discover and call them, as long as the two sides share a compatible transport and authentication flow. The analogy that stuck: MCP is USB-C for AI.

The problem it solves

A language model knows what was in its training data and nothing after. Every useful assistant therefore needs tools — search this, fetch that, query the other — and before MCP, each tool meant a bespoke integration per app. N apps × M data sources = N×M adapters. MCP collapses that to N+M: apps implement the protocol once as clients, data sources implement it once as servers, and everything interoperates. That economic logic, not any single feature, is why it became the standard.

How it works, mechanically

• A server advertises tools — named operations with typed, described inputs. The descriptions matter enormously: they're what the model reads when deciding which tool answers the user's question.
• The client connects — launching a local process, or reaching a remote server over HTTP.
• The model calls tools mid-conversation — you ask a question, the assistant picks a tool, sends arguments, gets structured results, and continues with real data in context.
• Auth follows the deployment. Local servers inherit your machine's access; remote servers authenticate with OAuth flows or API keys, exactly like any hosted API.

Local vs remote: where the ecosystem went

Early MCP servers were local processes. The ecosystem's weight has shifted to remote servers for anything data-shaped: nothing to install, always current, usable from web clients, and centrally authenticated. Public registries — the official MCP registry, plus directories with tens of thousands of listings — now index the landscape. For data businesses, a hosted MCP endpoint is becoming what a REST API was in 2015: table stakes for being consumable.

A concrete example: financial research over MCP

Octagon runs a remote MCP server at mcp.octagonai.co exposing its research agents as tools — SEC filings, earnings transcripts, stock and crypto data, and prediction-market research across Kalshi and Polymarket. Connected to Claude or Cursor, questions like "which markets repriced most today?" or "summarize this company's latest 10-K risk factors" become tool calls answered with live, cited data. Setup for each client is in the MCP server docs, and our comparison of the finance options is in best MCP servers for financial data.

If you're evaluating MCP servers

• Prefer remote with OAuth for data services — no dependency management, no stale versions, revocable access.
• Read the tool descriptions — they're the interface your model actually sees; vague descriptions produce wrong tool choices.
• Check the registry listing — the official registry and major directories are becoming the discovery layer, and a maintained listing is a decent proxy for a maintained server.

Frequently asked questions

What is an MCP server in plain English?

An MCP server is a small service that gives an AI assistant tools it can call — 'search these filings', 'fetch this market's odds', 'query this database'. MCP (Model Context Protocol) is the open standard that defines how the assistant discovers those tools and calls them, so MCP-capable apps — Claude, Cursor, and many others — can use MCP servers without per-pair custom integration, provided client and server support compatible protocol versions, transports, and auth flows.

What's the difference between MCP and a plugin or API?

An API is for programmers; a plugin is for one specific app. An MCP server is an adapter that describes an API in a way any AI client understands: it advertises named tools with typed inputs, the model chooses when to call them, and the same server works across MCP-capable clients that speak its transport. Write one server, reach nearly every assistant.

What's the difference between a local and a remote MCP server?

A local server runs on your machine (launched by the client, often via npx) and can touch local things like files. A remote server runs as a hosted service you connect to over HTTP, typically authenticating with OAuth or an API key — no install, always current, works from web clients. Data services increasingly ship as remote servers; Octagon's runs at mcp.octagonai.co.

Which apps support MCP servers?

Claude (desktop, web, and Claude Code), Cursor, Windsurf, Cline, and a fast-growing list of IDEs and agent frameworks. Support became a de facto industry standard after major AI vendors adopted the protocol in 2025, and public registries now index tens of thousands of servers.

What's a real example of using an MCP server?

Connect Octagon's MCP server to Claude and ask 'which prediction markets repriced most today, and why?' — the assistant calls the server's research tools, which query live Kalshi and Polymarket analysis, and answers with cited, current data instead of guessing from training memory. Same pattern for SEC filings, earnings transcripts, or stock data.