Unlocking Multi‑Agent AI with MCP and A2A

Artificial intelligence is rapidly evolving from single chatbots to complex networks of interacting agents that collaborate on multifaceted tasks. As this transformation takes hold, standard methods for communication and information exchange between these agents become crucial. Two open protocols – Anthropic’s Model Context Protocol (MCP) and Google’s Agent-to-Agent (A2A) protocol – are emerging to serve this new paradigm. Think of MCP as a "USB-C for AI" that plugs models into a plethora of tools and data sources, while A2A acts as a lingua franca enabling agents to converse and coordinate seamlessly.

In this post, we break down how these protocols work, discuss the technical and conceptual layers behind them, and provide practical implementation examples. We explore MCP’s role in giving AI models enhanced context and tool access and show how A2A enables discrete autonomous agents to collaborate on complex workflows. We also review industry insights, offer code-level examples, and outline the benefits and limitations. A real-world scenario demonstrates how the two protocols complement one another in multi-agent systems.

For developers and organizations building next-generation software, understanding these protocols is key. Platforms like Ardor – an AI-first cloud development environment – are part of the evolving ecosystem that supports modular, agentic software through integrated external tools and inter-agent communication.

The Model Context Protocol (MCP)

Overview and Concept

MCP, introduced by Anthropic in late 2024, is an open standard designed to streamline the way AI applications connect with external tools, data sources, and services. MCP provides a uniform interface that replaces bespoke and ad hoc integrations with a consistent JSON-RPC schema. Instead of writing custom connectors for each external service, developers can implement the MCP interface once. Anthropic famously describes it as a "USB-C for AI applications."

Using MCP, an AI model can access databases, file systems, APIs, or cloud storage without needing tailor-made integration code each time. This standardization reduces complexity, shortens development cycles, and improves overall security through controlled tool exposure.

Architecture and Communication Flow

MCP is built on a client–server model using standard web technologies such as HTTP and Streaming protocols. Its design leverages JSON-RPC 2.0, a lightweight format for remote procedure call messages. Three key roles in MCP are:

• MCP Server: A service that exposes capabilities or data (for example, a GitHub server for repository access)

• MCP Client: An AI application or agent that connects to one or more MCP servers

• Host Application: The environment running the model, such as Claude or ChatGPT, which mediates the connections

The communication is well defined. A client may request a list of available tools via methods like "tools/list" and invoke specific tools with "tools/call". Every interaction is encapsulated as a JSON-RPC message. For example:

{
"jsonrpc": "2.0",
"id": 1,
"method": "tools/call",
"params": {
	"name": "echo",
	"arguments": { "message": "Hello, world!" }
 }
}

The server processes the tool call and returns the result as another JSON message. This standardization simplifies the integration of data and tool contexts into AI responses.

Code-Level Example

Below is a simplified Python example using MCP. In this snippet, a client discovers available tools on the MCP server and then calls a tool named "github_recent_commits":

import requests, json

mcp_server_url = "http://localhost:8000/jsonrpc"  

# Discover tools
discover_payload = {
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/list",
  "params": {}
}
response = requests.post(mcp_server_url, json=discover_payload)
tools_catalog = response.json()
print("Available Tools:", json.dumps(tools_catalog, indent=2))

# Call a specific tool
call_payload = {
  "jsonrpc": "2.0",
  "id": 2,
  "method": "tools/call",
  "params": {
    "name": "github_recent_commits",
    "arguments": {"repo": "myorg/myrepo"}
  }
}
response = requests.post(mcp_server_url, json=call_payload)
result = response.json()
print("Tool Result:", json.dumps(result, indent=2))

This code demonstrates how MCP allows an AI model to remain tool-agnostic, eliminating the need for custom code for every integration. Early users include Block and developer tools like Replit and Sourcegraph.

Benefits and Limitations of MCP

MCP offers several benefits:

• Plug-and-play tool integration allows new tools or data sources to be added quickly.

• Enhanced model responses through access to real-time external data.

• A standardized approach that fosters ecosystem growth with prebuilt connectors for services like Slack, GitHub, and Notion.

• Improved security and auditing thanks to explicit JSON-RPC logging.

• Model-agnostic usage with compatibility on platforms like GPT-4, Claude, and Google Gemini.

There are also limitations:

• Added complexity may be overkill for simple tasks compared to local function calls.

• As an emerging standard, the MCP specifications may evolve, requiring developers to monitor updates and potential bugs.

• MCP provides the plumbing for tool connections but does not handle complex orchestration or full multi-agent coordination.

The Agent-to-Agent (A2A) Protocol

Overview and Concept

While MCP addresses the connection between an AI agent and its external tools, the Agent-to-Agent (A2A) protocol, announced by Google in April 2025, enables communication between multiple autonomous agents. A2A provides a standardized framework to let agents—possibly built by different vendors or using different frameworks—collaborate to solve complex, distributed tasks.

A2A uses familiar technologies such as JSON-RPC 2.0 over HTTP(S) and Server-Sent Events (SSE) for real-time updates. This approach supports scenarios where a team of specialized agents work together, each contributing its expertise to the final solution.

How A2A Works

A2A uses a client–server model where one agent acts as the client agent initiating a task and another acts as the remote agent fulfilling it. Central to this process is the Agent Card. An Agent Card is a JSON file hosted at a well-known URL (for example, "https://agent-domain/.well-known/agent.json") and contains metadata about the agent, including its name, version, endpoint URL, capabilities (or skills), and any authentication requirements.

For example, an agent’s card might indicate that it can provide weather forecasts or process reimbursements by listing its technical details.

Once an agent discovers another through its Agent Card, it can send a task request. These tasks may be synchronous for small jobs or asynchronous for tasks requiring streaming updates. A key advantage is that A2A supports multiple data modalities like text, images, and audio, creating a versatile communication layer for AI agents.

Code-Level Glimpse

A sample A2A workflow might involve a JSON payload that dispatches a reimbursement task to a remote agent:

POST <a href="http://localhost:10002/tasks" target="_blank" rel="noopener noreferrer" data-framer-link="Link:{"url":"http://localhost:10002/tasks","type":"url"}" data-framer-open-in-new-tab="">http://localhost:10002/tasks</a>

{
"id": "task-12345",
"skill": "process_reimbursement",
"input": { "amount": 20, "purpose": "team lunch" }
}

The remote agent would process the task and eventually return a status update or result through an HTTP response or an SSE event. This standardized process reduces overhead and enables interoperable, multi-agent systems.

Benefits and Limitations of A2A

A2A offers the following benefits:

• Dynamic agent collaboration that enables heterogeneous agents to work together on complex problems.

• Framework independence that allows agents from different companies or purposes to communicate using a shared protocol.

• Support for asynchronous, multi-modal communication suitable for long-running tasks.

• Enhanced security through standard authentication and authorization practices.

Some limitations of A2A include:

• As an early-stage implementation, the protocol may undergo significant changes before final maturity.

• Coordinating multiple agents adds complexity and requires robust orchestration and debugging tools.

• Increased infrastructure overhead may result from the need for persistent communication channels between agents.

Comparing MCP and A2A: Vertical vs. Horizontal Integration

MCP and A2A operate at distinct layers of the AI infrastructure:

• MCP (Vertical Integration) focuses on connecting an AI agent with external tools and data sources. It provides a plug-and-play interface to give agents additional context and operational capability.

• A2A (Horizontal Integration) facilitates communication among different agents, allowing them to collaborate, delegate tasks, and exchange information. This horizontal layer is crucial when tasks span multiple specialized agents.

In practice, many systems benefit from using both protocols. For example, an AI system might use MCP for a single agent to retrieve real-time data from a database and A2A for coordinating with another agent that processes the data and generates a report.

Real-World Scenario: Using MCP and A2A Together

Imagine a corporate environment where multiple AI agents form a distributed ecosystem. Consider the following agents:

• Email Agent: Reads and sends emails using MCP to interface with email servers and scheduling APIs.

• Database Agent: Retrieves structured data through an MCP server connected to an SQL database.

• Report Generator Agent: Produces formatted reports by leveraging MCP tools for charting and document templating.

• Manager Agent: Orchestrates high-level requests from a human manager and delegates tasks accordingly.

For example, if a human manager asks, "What were our sales figures last quarter, and can you email me a summary with the top client highlights?" the system might respond as follows:

• Coordination via A2A: The Manager Agent sends a task to the Database Agent using A2A to retrieve last quarter’s sales figures. The Database Agent queries the database through its MCP interface and returns the results via A2A.

• Generating Insights: The Manager Agent forwards the retrieved sales data to the Report Generator Agent via A2A. The Report Generator Agent uses MCP tools, such as a charting tool, to generate a detailed PDF report.

• Final Task Execution: Once the report is ready, the Manager Agent directs the Email Agent via A2A to email the report to the manager.

In this scenario, MCP serves as the backbone for each agent’s individual tool integration while A2A manages the inter-agent communications to form a coherent system. This modular and orchestrated approach reduces complexity and enhances system robustness. If one agent is unavailable, another with similar capabilities can be discovered and employed with minimal disruption.

Industry Adoption and Perspectives

Both MCP and A2A have received support from major industry players:

• Anthropic (Claude) developed MCP, which is integral to their products. Large-scale integrations, including those in developer tools, rely on MCP for seamless access to external data.

• Google (DeepMind) launched A2A with over 50 partners, positioning the protocol as a supplement to MCP. Google’s upcoming Gemini models are expected to support MCP, demonstrating cross-industry endorsement.

• OpenAI, known for plugins and function-calling capabilities, recently announced experimental support for MCP in its Agents SDK. This unification under open standards is reshaping how AI models interact with real-world data.

• Frameworks like LangChain and projects such as AutoGPT are leveraging these protocols to provide higher-level orchestration for AI agents.

• Enterprise vendors such as Salesforce, IBM, and Cisco are evaluating both protocols. For instance, Salesforce’s CTO has discussed MCP’s role in revolutionizing AI integration across platforms, while Cisco is actively exploring standardized agent communication.

The convergence of these standards represents a significant milestone. Whereas traditional REST APIs and OpenAI’s plugin system provided isolated tools, MCP and A2A unify integration and communication into a comprehensive framework, much like HTTP standardized web communications. Platforms like Ardor are working toward an ecosystem where AI agents and orchestrated multi-agent systems become part of the standard development lifecycle.

Comparisons to Similar Platforms and Technologies

Before MCP and A2A, AI integrations relied on bespoke APIs and early plugin systems. Here’s how previous approaches compare:

• Traditional REST APIs offer powerful service-specific communication but require custom integration for each tool.

• Plugin frameworks (such as ChatGPT’s plugins) are designed for single-agent enhancement but lack modularity for multi-agent systems.

• Microservice architectures rely on standardized communication between services. Similarly, MCP abstracts tool access while A2A offers a parallel for inter-agent collaboration.

• Agentic cloud platforms like Ardor also emphasize an agent-first approach, complementing the modular interoperability enabled by MCP and A2A.

Benefits and Limitations Recap

MCP Benefits:

• Simplified integration with external data and tools through a standardized JSON-RPC mechanism

• Reduced risk of context fragmentation with multiple integrations

• Enhanced accuracy via access to ground truth data

MCP Limitations:

• Additional infrastructure complexity for simple use cases

• Evolving best practices and potential issues due to its emerging nature

A2A Benefits:

• Empowers distributed agent collaboration for complex multi-step tasks

• Enables modular, scalable workflows similar to modern microservices

• Supports asynchronous, multi-modal exchanges for long-running tasks

A2A Limitations:

• As an early-stage technology, some aspects of the protocol are still being refined

• Coordination overhead may increase resource demands and complicate debugging

FAQ

Here are some frequently asked questions about these protocols:

When should I use MCP versus A2A? Do I need both?

Use MCP when an AI agent needs to access external tools (e.g., databases or file systems). Use A2A when multiple agents need to collaborate and exchange tasks. In many applications, both protocols are used together, with each agent using MCP for resource access and A2A for peer communication.

How do I implement MCP in my project?

Implementing MCP typically involves running or integrating an MCP server for each external tool and embedding an MCP client in the AI agent. SDKs are available in Python, TypeScript, Java, and other languages.

How is A2A implemented?

A2A requires each agent to host an Agent Card (a JSON metadata file) and implement HTTP-based task endpoints. Once discovered, agents exchange task requests and stream status updates using JSON-RPC and SSE.

What about security and authentication?

Both protocols are designed with security in mind. MCP can enforce API keys and sandbox access to tools, while A2A supports modern security methods such as OAuth and mutual TLS for authenticating and authorizing agents.

How do these compare to OpenAI’s plugins?

OpenAI’s plugins and function calling are proprietary methods for tool integration. MCP generalizes this concept for any AI platform, and A2A fills the gap in multi-agent communication—capabilities that traditional plugins do not fully cover.

Conclusion and Next Steps

MCP and A2A represent a significant evolution in AI infrastructure. MCP empowers individual agents by providing on-demand access to external tools and real-time data, while A2A creates the framework for true multi-agent collaboration. Together, they pave the way for robust, modular, and scalable AI systems—a revolution already gaining traction among tech giants and startups.

For developers and engineers, now is the time to experiment with these protocols. Whether you are building new software or integrating AI into existing systems, implementing MCP and A2A can significantly shorten development cycles and enhance system resilience. Platforms like Ardor exemplify the agent-first approach that integrates these protocols into an AI-native cloud environment, streamlining development for both technical and non-technical users.

As this ecosystem evolves, staying engaged with community channels—such as GitHub, developer blogs, and forums—will be crucial. The coming years promise a revolution in how AI agents interact, much like HTTP transformed web services.

References

• A2A and MCP: Start of the AI Agent Protocol Wars? - Koyeb

• A2A vs MCP: Two complementary protocols for the emerging agent ecosystem · Logto blog

• Model Context Protocol: What You Need To Know - Gradient Flow

• Introducing the Model Context Protocol \ Anthropic

• Announcing the Agent2Agent Protocol (A2A) - Google Developers Blog

• OpenAI adopts rival Anthropic's standard for connecting AI models to data | TechCrunch

• Agent to Agent Protocol - Helping AI Agents Work Together

Final Thoughts

The future of AI is not about isolated capabilities—it is about interconnected systems that combine individual strengths into a robust, collaborative intelligence. By adopting MCP for tool integration and A2A for inter-agent communication, you can position your organization at the forefront of the agentic revolution. Experiment with these protocols, iterate on your implementations, and join the community discussion to shape the next chapter in AI development.

• If you’re excited to build or explore multi-agent AI systems, start by studying these protocols in depth.

• Experiment with MCP and A2A in your projects.

• Join community forums and consider platforms like Ardor for a cloud-native, agent-first development experience.

The future waits for no one—empower your applications with the next wave of intelligent collaboration!