MCP vs. A2A: AI Protocols Compared

Forget everything you know about AI integration chaos. Model Context Protocol (MCP) and Agent2Agent (A2A) have recently rewritten the rules – no more custom code for every third-party tool and no more agent coordination headaches. These two protocols address AI's connectivity crisis from different angles, and together, they're saving significant development time.

This guide cuts through the complexity to explain how each protocol works, compares MCP vs. A2A, and suggests when to deploy MCP or A2A.

What is MCP?

Model Context Protocol solves the critical problem of equipping LLMs with tools and data sources. Before it existed, connecting AI to each tool required custom code. Want your AI to access Slack? Learn Slack's API. Need database access? Write an SQL integration. Every connection was a separate project.

Anthropic’s MCP changes this by providing a universal protocol. When AI is connected to an MCP server through an MCP client, it can discover available tools, learn their capabilities, use these tools, and get results in an AI-friendly format. In short, LLMs can connect to external tools and sources in a standardized and automatic way that eliminates the need for custom integrations on the user’s end.

MCP flow example

Consider this simplified flow of an AI agent that can extract web data using the Oxylabs MCP server:

1. User’s prompt: "Is the Samsung Galaxy S25 Ultra sold on Amazon?". The AI agent recognizes it needs current Amazon search data.

2. Tool and capability discovery: The agent uses Oxylabs MCP server and discovers four available scrapers: Universal, Google Search, Amazon Search, and Amazon Product. The AI chooses the Amazon Search scraper and sends a request with the query set to "Samsung Galaxy S25 Ultra".

3. Request processing: Oxylabs Web Scraper API processes the request, navigates Amazon, and returns relevant data through the MCP protocol.

4. Result analysis: The agent receives clean data and answers the user’s question with fresh information from Amazon.

Key features of MCP

• Extends AI capabilities: AI can discover and easily use third-party tools, resources, and prompts.

• Universal and vendor-neutral: AI with an MCP client works out of the box with any tool or resource hosted on an MCP server.

• Real-time data: Connects AI to live sources instead of relying on training data.

• Fast-growing ecosystem: 150+ official MCP servers with integrations for Microsoft, GitHub, and Cloudflare, as well as 350+ community-driven servers.

MCP use cases

From accessing internal knowledge bases to letting AI schedule meetings with clients, the adoption of MCP is broad. Some prominent use cases include:

• Data extraction: Letting any AI model get fresh information from websites (see our Agents SDK setup and LangChain integration).

• Development automation: AI that can query your actual database schema before writing SQL queries.

• Business workflows: AI that books meetings, updates CRM, or sends emails to customers.

While MCP makes AI agents smarter, there’s only so much a single agent can do. Complex workflows involve multiple agents, and this is where the A2A protocol comes into play.

What is A2A?

Agent2Agent, developed by Google, tackles a different challenge: making any remote agent work together with other agents. In the real world, complex problems require specialists. A lawyer doesn't perform surgery, and a surgeon doesn't argue court cases. They collaborate when needed.

A2A brings this model to AI with an inter-agent communication protocol. Instead of one agent trying to do everything, you have specialized agents who excel at specific tasks. The protocol lets agents find each other and collaborate while keeping their internal workings private. The major benefit of the agent-to-agent protocol is that it lets agents interact regardless of the framework they’re built on.

A2A flow example

Let’s visualize agent orchestration via A2A for performing an in-depth SEO analysis, which a single agent couldn’t handle alone with confidence:

1. User’s prompt: "Draft a comprehensive SEO analysis report using the top 5 ranking blog posts on Google that analyze the best RAG tools".

2. Agent discovery: The client agent formulates the requirements and uses the A2A server to perform agent discovery via "Agent Cards" that fit this task best. It finds Website Scraper Agent and SEO Analysis Agent.

3. 1st agent execution: The A2A server sends a request to the Website Scraper Agent. It picks the Google Search Scraper from its MCP server and extracts the top 5 ranking websites for the search query "best RAG tools". Then, it scrapes all 5 blog posts using the Universal Scraper and returns their content.

4. 2nd agent execution: The A2A server sends scraped Google results and blog posts to the SEO Analysis Agent. It uses all the provided data and internal tools from its MCP server to analyze and draft a comprehensive SEO report with key insights.

5. Final result: The A2A client gets the final SEO report from the A2A server and passes it back to the user.

Key features of A2A

• Multi-agent collaboration: Enables autonomous agents to work together on complex, long-running tasks while delegating tasks, sharing context, progress, and results throughout the process.

• Discovery through Agent Cards: Agents can discover each other's identity, skills, agent's capabilities, authentication requirements, and connection details.

• Multimodal communication: Agents can negotiate which data formats they support (text, files, media, structured data).

• Security: Uses modern web security standards while ensuring agents collaborate without exposing internal memory, proprietary logic, and tool implementations.

A2A use cases

As with MCP, the use cases of A2A are only limited by imagination. For instance, some specific A2A use cases include the following:

• Deep researcher: An agentic workflow that scrapes data from different search domains, checks source trustability, aggregates content, and writes comprehensive reports.

• Infosec analyzer: A single agent manages a multi-agent ecosystem that performs vulnerability research, penetration testing, and drafts security reports about a web application.

• Travel planner: An orchestration of different agents that find the best travel deals, plan the trip, and book flights, car rentals, train tickets, hotels, and activities.

A2A and MCP compared