Model Context Protocol

How MCP stacks up against traditional communication protocols

Where MCP seems familiar

JSON-RPC foundation

MCP’s messaging layer builds on JSON-RPC 2.0, so you’ll recognize:

• structured method calls and responses
• JSON-based serialization
• standard error handling
• notification and request-response patterns

HTTP transport compatibility

The HTTP transport option makes MCP easy to deploy over familiar web infrastructure:

• client-to-server messages use POST
• server-to-client streaming uses Server-Sent Events (SSE)
• supports standard HTTP authentication (bearer tokens, API keys)
• reuses HTTP networking features such as proxies and TLS

Local / stdio (“WebSocket-like”) transport

For local integrations, MCP also supports a stdio transport, offering:

• persistent, bidirectional message channels
• low-latency exchange between local processes
• event-driven interactions, ideal for real-time tooling

Where MCP breaks new ground

Semantic abstraction layer

MCP introduces a set of high-level primitives tailored for AI-tool interaction:

• Tools — executable operations defined with schemas (APIs / functions the model can invoke)
• Resources — contextual data sources (files, DB schemas, documents, metadata) exposed to the model
• Prompts — reusable templates or instruction scaffolds offered by the server
• Sampling — model completion requests (text, image, audio) under protocol control

These primitives provide a richer semantic interface compared to raw HTTP or JSON-RPC endpoints, aligning more directly with AI workflows.

AI-centric lifecycle & discovery

MCP defines a richer lifecycle and discovery process than traditional RPC or REST systems:

• Capability negotiation — client and server agree on supported features such as tools, resources, and prompts.
• Dynamic discovery — clients can query available primitives through standardized list methods.
• Context-aware state management — maintains session context, negotiated features, and resource state across interactions.
• AI integration patterns — provides conventions for exposing prompts, resources, and structured tools optimized for AI-driven workflows.

Domain-specific design

Unlike general-purpose protocols like REST or gRPC, which aim to serve any system, MCP is optimized for AI integration. It focuses on:

• Context-rich model interactions – standardizing how AI models discover and use external tools and data.
• Model–system decoupling – enabling interchangeable tools through consistent schemas.
• Agentic workflow orchestration – supporting multi-step, autonomous reasoning and execution.
• Modality-agnostic integration – allowing consistent access to text, image, or audio tools.

Traditional protocols move data; MCP enables cooperation between models and systems, trading universality for interoperability, semantics, and autonomy.

How MCP compares to other AI agent protocols

LangChain & MCP: cooperation rather than competition

Shared goals

• Both aim to reduce integration friction between agents and external systems
• Use JSON-based schemas and communication
• Support agent lifecycle logic (tool use, state, chaining)
• Open-source

Key divergences

• Focus / scope: LangChain is an agent orchestration / tool-chaining framework; MCP is a protocol for exposing context, tools, and resources to agents
• Abstraction layer: LangChain is built around “chains, agents, memory, tool routing”; MCP defines primitives (tools, resources, prompts, sampling) at the protocol boundary
• Role in architecture: LangChain manages how agents reason and plan; MCP standardizes how agents invoke external systems

A2A & MCP: horizontal + vertical integration

Complementary roles

Google positions A2A as a protocol for agent-to-agent coordination, while MCP provides the plumbing for an agent’s access to tools, data, and context. oai_citation:4‡Google Developers Blog

Distinct design emphases

• Agent vs system interface: A2A emphasizes peer communication and shared tasks; MCP emphasizes structured tool/data invocation
• Discovery & capabilities: A2A uses “Agent Cards” and capability exchange between agents; MCP uses listing/discovery primitives for tools, resources, prompts
• Modality support: A2A is designed to support diverse modalities in agent dialogue; MCP is protocol-agnostic but its implementations often start with text/JSON-based tooling

Architecture comparison

How the protocol stacks actually work

MCP architecture

Application Layer: AI Host (Claude, VS Code, etc.)
    ↓
Client Layer: MCP Client (one per MCP Server)
    ↓
Data Layer: JSON-RPC 2.0 + MCP Primitives (tools, resources, prompts, sampling)
    ↓
Transport Layer: stdio | HTTP + SSE
    ↓
Network Layer: TCP/IP

Traditional REST API

Application Layer: Web Application
    ↓
API Layer: REST Endpoints (CRUD + HTTP Methods)
    ↓
Transport Layer: HTTP/HTTPS
    ↓
Network Layer: TCP/IP

A2A protocol

Application Layer: Agent Ecosystem
    ↓
Agent Layer: Client Agent ↔ Remote Agent
    ↓
Protocol Layer: JSON-RPC + A2A Extensions (Agent Cards, Capabilities)
    ↓
Transport Layer: HTTP + SSE
    ↓
Network Layer: TCP/IP

How messages actually flow through these systems

Here’s how MCP handles message flow

1. Initialization – handshake and capability negotiation
2. Discovery – list available primitives (tools/list, resources/list, prompts/list)
3. Context Retrieval – fetch context objects (resources/get, prompts/get)
4. Action Execution – call executable tools (tools/call)
5. Streaming / Notifications – receive updates, results, or errors

Traditional API flow

1. Authentication
2. Request construction
3. HTTP execution (GET, POST, etc.)
4. Response parsing
5. Connection termination

A2A message flow

1. Agent Discovery – identify agents and advertise capabilities
2. Task Delegation – assign or request task execution
3. Collaboration – message exchange between agents
4. Artifact Sharing – exchange intermediate or final outputs
5. Completion & Feedback – finalize tasks, optionally update shared state