필사 모드: The Model Context Protocol (MCP): An Engineer's Reference

• Introduction — what MCP is, and the problem it solves
• Architecture — hosts, clients, servers, and transports
• The three primitives — tools, resources, prompts
• Building a server — how to think about it
• Tradeoffs and maturity — an honest read
• Closing
• References

Introduction — what MCP is, and the problem it solves

In the words of the official docs, the Model Context Protocol (MCP) is "an open-source standard for connecting AI applications to external systems." It gives applications like Claude or ChatGPT a standardized way to connect to data sources (local files, databases), tools (search, calculators), and workflows (specialized prompts). The docs offer a memorable analogy: think of MCP as "a USB-C port for AI applications" — one connector standard instead of a bespoke adapter per device.

Why standardize? The usual framing is the M×N problem. If you have M AI applications and N tools or data sources, then without a standard you write M×N bespoke integrations by hand. A shared protocol collapses that to M+N — build a server once and every protocol-speaking client can use it, and a client that implements the protocol can reach every server. The docs put it as "build once and integrate everywhere." MCP borrows this idea from the Language Server Protocol (LSP): where LSP standardized the "editor × language" matrix, MCP standardizes the "AI app × tool" matrix.

MCP was introduced and open-sourced by Anthropic in November 2024, shipping initially with Python and TypeScript SDKs and prebuilt servers for Google Drive, Slack, GitHub, Git, Postgres, and Puppeteer. It was subsequently adopted by OpenAI and Google DeepMind, making it a de facto industry standard.

Architecture — hosts, clients, servers, and transports

MCP follows a client-server architecture with three participants:

• Host. The AI application that coordinates and manages one or more MCP clients (e.g., Claude Code, VS Code).
• Client. A component that maintains a connection to a single server and obtains context from it.
• Server. A program that provides context to clients. Whether it runs locally or remotely, it is still "the server."

The key rule is one client per server. The host instantiates a dedicated client for each server it connects to, and each client keeps its own independent connection. If VS Code connects to a filesystem server and a Sentry server, two client objects exist internally.

The protocol has two layers. The data layer is a JSON-RPC 2.0 exchange protocol that defines lifecycle management and the primitives (tools, resources, prompts, notifications); the transport layer handles the actual communication channels and authentication. Connections are stateful and begin with a handshake: the client sends an initialize request to negotiate the protocolVersion and each side's capabilities, then signals readiness with a notifications/initialized message.

There are two transports:

• stdio. Standard input/output for direct process-to-process communication on the same machine — no network overhead, ideal for local servers, and typically serving a single client.
• Streamable HTTP. HTTP POST for client-to-server messages, with optional Server-Sent Events (SSE) when streaming is needed. This is the remote transport; it supports bearer tokens, API keys, and custom headers, and the docs recommend OAuth for obtaining tokens.

The transport layer abstracts these details away so the same JSON-RPC message format works across any transport.

The three primitives — tools, resources, prompts

Primitives are the most important concept in MCP, and the three a server exposes are the core of it. They differ in who controls them:

• Tools — model-controlled. Functions the AI actively invokes to perform actions (write files, call APIs, query databases). Inputs are validated with JSON Schema. Discover with tools/list, execute with tools/call. Because the model decides when to call them, human oversight — such as approval before execution — is emphasized.
• Resources — application-controlled. Read-only context data (file contents, database schemas, documents). Each resource has a unique URI and a MIME type. There are direct resources with fixed URIs and resource templates that take parameters. Methods: resources/list, resources/templates/list, resources/read, and resources/subscribe for change monitoring.
• Prompts — user-controlled. Reusable templates that show how to combine tools and resources. Like slash commands, they are invoked explicitly by the user. Methods: prompts/list, prompts/get.

Resource URIs come in two shapes — fixed (direct resources) and parameterized (templates), illustrated below:

file:///Users/me/notes.md          # direct resource (fixed URI)
calendar://events/2026             # direct resource
weather://forecast/{city}/{date}   # resource template (parameters)

The design follows a regular pattern — discovery via */list, retrieval via */get, and execution via tools/call. Listings are dynamic, so when a server's tools change it can notify clients with messages like notifications/tools/list_changed.

Conversely, clients can expose primitives to servers:

• Sampling. sampling/createMessage lets a server request a completion from the client's LLM — so the server can use a model without bundling its own model SDK.
• Elicitation. elicitation/create lets a server request additional input or confirmation from the user.
• Roots and logging. Roots tell the server which file/URI boundaries it may operate in; logging carries debug messages back to the client.

Building a server — how to think about it

Building a server is really about deciding what to expose as a tool, a resource, or a prompt. Actions become tools, readable context becomes resources, and structured workflows become prompts. The official SDKs are TypeScript, Python, C#, and Go (Tier 1); Java and Rust (Tier 2); Swift, Ruby, PHP, and Kotlin (Tier 3). All provide the same functionality following each language's idioms, so exact APIs differ — check the per-language docs.

A tool definition is a name, a description, and an input schema. The shape from the docs (illustrative):

{
  "name": "searchFlights",
  "description": "Search for available flights",
  "inputSchema": {
    "type": "object",
    "properties": {
      "origin": { "type": "string", "description": "Departure city" },
      "destination": { "type": "string", "description": "Arrival city" }
    },
    "required": ["origin", "destination"]
  }
}

The client first fetches these definitions via tools/list and registers them with the LLM; when the model picks a tool, the client executes it via tools/call. The JSON-RPC on the wire looks like this (illustrative):

{ "jsonrpc": "2.0", "id": 3, "method": "tools/call",
  "params": { "name": "weather_current",
              "arguments": { "location": "San Francisco", "units": "imperial" } } }

The response comes back as a content array that can carry multiple formats — text, images, resources. For development and debugging, the official MCP Inspector and the reference server collection are good starting points.

Tradeoffs and maturity — an honest read

The appeal is clear: integrate once, and the whole ecosystem shares one format, LSP-style. But there are things to look at soberly.

• Security is not baked into the protocol. The spec itself states that tools "represent arbitrary code execution" and that a tool's descriptions or annotations "should be considered untrusted" unless the server is trusted. User consent, tool-execution approval, and sampling approval are the host's job — so safety lives in the client, not the protocol.
• Remote authentication is still heavy. Streamable HTTP plus OAuth is powerful, but a long way from the simplicity of local stdio.
• The spec keeps moving. Versions are date-based (the latest is 2025-11-25), and features like Tasks are still Experimental. SDKs are split into tiers, so maturity varies by language.
• Governance is in transition. Per reporting, in December 2025 MCP moved to the Agentic AI Foundation under the Linux Foundation — a signal of the shift from one company's project toward a neutral standard.

Closing

The core of MCP is not a flashy new technology but "boring standardization" — and that is precisely its value. Understand three primitives (tools, resources, prompts) and one thin format (JSON-RPC), and any client connects to any server the same way. If you are an engineer wiring agents into real systems, understanding this one protocol layer will outlast whichever framework you pick on top of it.

References

• Introducing MCP — modelcontextprotocol.io
• Architecture overview
• Server concepts — tools, resources, prompts
• Specification (latest)
• Official SDKs
• Anthropic's original announcement
• Model Context Protocol — Wikipedia