Engineering40 min read

Comparing Anthropic’s Model Context Protocol (MCP) vs Google’s Agent-to-Agent (A2A) for AI Agents in Business Automation

Anthropic’s MCP connects agents to tools and context. Google’s A2A connects agents to each other. This deep dive explores how both frameworks shape the future of AI agents in business automation — where they compete, where they complement, and how to use them together.

Tega Adeyemi
Tega Adeyemi
Comparing Anthropic’s Model Context Protocol (MCP) vs Google’s Agent-to-Agent (A2A) for AI Agents in Business Automation

Business process automation with AI often involves multiple agents (AI systems) that need access to tools and data, and sometimes need to collaborate with each other. Two emerging frameworks address these needs: Anthropic’s Model Context Protocol (MCP) and Google’s Agent-to-Agent (A2A). In simple terms, MCP focuses on connecting AI agents to external tools and context (vertical integration), while A2A focuses on communication between agents themselves (horizontal integration). This article provides a deep dive into both MCP and A2A, comparing their architecture, communication methods, integration capabilities, performance in complex workflows, code examples, and how they can complement each other in automating business processes.

Architectural Overview of MCP and A2A

MCP Architecture: The Model Context Protocol uses a client-server architecture to bridge AI models with data sources and tools​ An MCP Host (e.g. a chat interface like Claude Desktop or an IDE assistant) contains an MCP Client that manages connections to one or more MCP Servers. Each MCP server exposes a specific integration – for example, a filesystem, database, or API – through a standardized interface. The host (AI agent) can retrieve context or invoke operations via these servers. Think of MCP as a “USB-C port” for AI – a universal plug that lets the AI connect to many peripherals (tools/data sources) in a standard way​.

Comparing Anthropic’s Model Context Protocol (MCP) vs Google’s Agent-to-Agent (A2A) for AI Agents in Business Automation — Tega Adeyemi | Cohorte
Overview of MCP’s client-server architecture. An AI application with an MCP client connects (via the MCP protocol) to multiple MCP servers (A, B, C), each interfacing with different local or remote data sources.

Each MCP server typically offers three kinds of capabilities to the AI agent:

MCP servers advertise their available tools, resources, and prompts, and the MCP client performs a handshake and discovery process to list these capabilities. The host can then make these available to the AI model (for example, by formatting tools as function-call options for the LLM). When the AI needs to use a tool or fetch a resource, the MCP client sends a request to the corresponding server and gets back the result. Communication can occur via Stdio (standard I/O) for local servers or HTTP + Server-Sent Events (SSE) for remote servers, both following a JSON-RPC 2.0 message format. This allows flexible deployment – e.g., running a tool integration as a local subprocess or as a cloud service.

A2A Architecture: Google’s Agent-to-Agent framework is an open protocol for direct agent communication across different platforms and organizations​github.com​github.com. Instead of connecting to tools, an agent in A2A connects to other agents through a standard task-oriented message protocol. Each agent that supports A2A exposes an A2A Server endpoint (usually an HTTP API) and publishes an Agent Card – a JSON metadata file (often at a URL like /.well-known/agent.json) describing the agent’s capabilities (“skills”), API endpoint, and auth requirements. Another agent can act as an A2A Client by discovering this card and then sending task requests to the server.

Comparing Anthropic’s Model Context Protocol (MCP) vs Google’s Agent-to-Agent (A2A) for AI Agents in Business Automation — Tega Adeyemi | Cohorte
Simplified view of A2A communication. A “Client Agent A” can connect to a “Remote Agent B” via the A2A protocol. Agents advertise capabilities through Agent Cards (JSON). Communication between agents is structured as tasks, with agents exchanging messages (context, responses, artifacts, etc.) to coordinate.

In A2A, all interactions are wrapped in a Task lifecycle. One agent (the client in that interaction) creates a task with a unique ID and an initial message, sending it via a tasks/send request to the other agent (the remote agent). The message typically contains the user’s request or some instruction, packaged into one or more Parts (each part can be text, a file, or structured data). The remote agent processes the task – possibly involving its own reasoning or tool use – and replies with Messages and/or Artifacts. Messages represent the dialogue turns (for example, the agent asking for clarification would send a message with role: "agent" and maybe a question). Artifacts are outputs like files or final results produced during the task. The task has states such as submitted, working, input-required (if the agent needs more info from the user or client agent), completed, etc., which allow for complex, multi-turn workflows.

A2A’s design builds on web standards – it uses HTTP(S) + JSON (JSON-RPC 2.0) for requests, and supports SSE for streaming live updates and webhooks (push notifications) for long-running tasks​. This means an agent can stream partial results or status updates back to the caller, or notify when a long job is done, without the caller polling constantly​. The protocol also emphasizes capability discovery (via the agent cards) and negotiation of interaction modality – agents can even negotiate how to interact (text, forms, audio, etc.) if needed​. Security is considered as well (support for authentication schemes similar to OpenAPI).

Communication, Coordination, and Memory Handling

MCP: Context Injection and Tool Invocation

MCP is fundamentally about providing context and actions to an LLM-based agent. Communication in MCP occurs between the client (in the host/agent) and the server (integration). MCP defines request-response messages for things like listing tools (tools/list), calling a tool (tools/call), reading a resource (resources/read), etc., as well as one-way notifications. Under the hood it follows JSON-RPC 2.0, which includes standard fields for request IDs, method names, params, and error handling. This structured messaging means the AI agent can programmatically query what a tool can do and invoke it through a well-defined interface, rather than relying on brittle prompt strings.

To coordinate with the AI’s reasoning process, many MCP implementations make use of the LLM’s function calling or tool-use paradigm. For example, the OpenAI Agents SDK integrates MCP servers by treating each MCP tool as an available function for the model. At runtime, the agent will automatically call call_tool() on the MCP server when the model decides to use that function. The MCP client handles this call and waits for the server’s response (e.g., the result of a database query), which it then returns to the model.

Context/Memory in MCP: Since LLMs have a limited context window, MCP helps fetch relevant context on demand. Resources serve as an external memory – the agent can retrieve documents, knowledge base entries, or other data via resource URIs (e.g., file:// paths, database keys, etc.). The client or user typically chooses which resources to pull in, ensuring the model gets only the relevant snippets (to avoid overloading context). In some cases, the AI itself might be allowed to select resources or ask for them, but the protocol encourages an app or user to stay in control of context provisioning for safety. For a more agent-driven approach (where the AI can decide to fetch data), developers can expose those data sources as tools instead. For example, rather than automatically reading a huge database, the AI might have a search_db tool it can call with a query – under the hood it returns a piece of data, but it’s treated as a tool invocation (model-controlled) so the AI triggers it only when needed.

MCP does not enforce a particular memory persistence across sessions – that is usually handled by the AI application. However, the MCP ecosystem includes examples of a “Memory” server that provides a knowledge graph or vector store for long-term memory​. Such a server can let an agent store and retrieve embeddings or facts, effectively extending the agent’s memory beyond a single context window. Since MCP servers can be written to interface with any backend, developers have created integrations for vector databases like Qdrant and Weaviate to support semantic search as a tool​. This means an AI agent could, for instance, call a vector_search tool via MCP to find related information in a vector DB, achieving retrieval-augmented generation (RAG) in a standardized way.

In summary, MCP handles coordination by giving the agent structured access to tools and data. The agent’s core reasoning loop remains within the LLM, but MCP intermediates when the agent needs outside help. The protocol ensures the agent’s requests to tools are linked to responses (through request IDs) and can handle errors gracefully (via JSON-RPC error objects). Multiple MCP servers can be connected simultaneously, but generally the agent will invoke one tool at a time as needed in its chain of thought (the client multiplexes requests to the correct server).

A2A: Agent Collaboration and Task Management

A2A handles coordination at a higher level – agents coordinating with other agents. Communication in A2A is centered on the Task model. When Agent A needs help from Agent B, Agent A (as a client) sends a tasks/send request containing a new task ID and an initial message. The message could be something like: “User asks: What are the sales figures for last quarter?” if Agent A is delegating to a specialized “analytics agent”, for example. Agent B, upon receiving this, will generate a response (or series of messages) trying to fulfill the task.

During a task’s lifecycle, agents exchange Messages that carry the conversation forward. Each message has a role (either "user" or "agent" from the perspective of the task)​. For instance, Agent B might send back a message saying, “I can retrieve the data. Do you want it broken down by region?” – marked as role "agent" asking the user (Agent A, acting on the user’s behalf) for input. This would put the task in an input-required state. Then Agent A can send another tasks/send with the additional info (e.g., “Yes, breakdown by region.”). This back-and-forth continues until Agent B signals completion with a final answer or result.

One powerful aspect of A2A is that agents do not share their internal chain-of-thought or memory – they communicate only via these structured messages and artifacts. Google describes this as “opaque execution” – each agent remains a black box in terms of how it computes the answer. They only share the necessary inputs/outputs. This ensures that, for example, proprietary reasoning or confidential data an agent has in memory is not directly exposed, unless deliberately sent as a message or artifact. It also means each agent can have its own tools or context (perhaps using MCP internally!) and only the end result is exchanged. In other words, A2A treats each agent almost like an autonomous web service – one that can chat and exchange files – rather than merging their internals.

The A2A protocol supports multiple concurrency and long-running coordination patterns. For long tasks, instead of a simple request/response, the Server-Sent Events (SSE) channel allows the remote agent to stream TaskStatusUpdate events (e.g., “20% complete”) and TaskArtifactUpdate events (intermediate outputs) to the client. If a task might take a very long time (say hours or days, with a human eventually providing input), A2A servers can also use push notifications – the client can set a webhook where it wants updates, and the server will POST updates or results there, so the agents can disconnect and not hold the HTTP connection open. This design acknowledges that in enterprise workflows, an agent might delegate a task that involves waiting for human approval or an external process; A2A can handle that reliably by marking the task state as working or waiting and resuming when ready.

Coordination and roles: In any A2A interaction, one agent is initiating (client role) and the other is fulfilling (server role). But these roles are flexible and can switch. Agent A might call Agent B for one task, and later Agent B could call Agent A (if Agent A exposes A2A server capabilities) for a different task. This allows peer-to-peer collaboration. In practice, for clarity, many architectures will have a primary orchestrator agent that delegates subtasks to specialist agents. But A2A doesn’t enforce a hierarchy; any agent that knows another’s endpoint and has permission can send tasks.

Memory in A2A: Each agent keeps its own memory of tasks it is handling. The A2A protocol itself doesn’t specify how an agent should remember context, only that within a task, the history of messages is logically tracked (the agent can infer context from prior messages in that task’s thread). An agent implementation could store the conversation history of a task ID and use it when generating new responses. Because tasks have IDs, even if communication is intermittent, an agent can look up the state (messages and artifacts so far) of that task and continue. This is crucial for reliability – if a network hiccup occurs, the client can poll the tasks/status or simply retry tasks/send with the same task ID and last message, and the server knows it’s part of an ongoing interaction.

The isolation of memory per agent means if broader shared memory is needed, it has to be through explicit communication. For example, if Agent A wants Agent B to use some data A has, A must send it as part of the task (maybe as a FilePart or DataPart in a message). This clear delineation can actually aid security and debugging, since all cross-agent info exchange is visible in the task messages.

In summary, A2A provides a structured yet flexible way for agents to coordinate on tasks without merging their internal states. Agents coordinate by negotiating tasks: the client agent specifies what it wants, the remote agent can ask for clarifications or provide partial results, and ultimately returns a completion or failure. All of this happens with standardized message types and state transitions that any A2A-compliant agent can understand.

Integration and Extensibility with LLMs and Tools

Integration with LLMs and Agent Frameworks

Both MCP and A2A are designed to be model-agnostic and framework-agnostic, meaning they can work with various large language models (LLMs) or agent frameworks, as long as those can interface with their protocols.

Tool and External System Extensibility

Performance, Flexibility, and Reliability in Complex Workflows

Long-Running Workflows and Multi-Agent Environments

In practical business processes, tasks might be complex, involving many steps or waiting for events. Let’s compare how MCP and A2A handle these scenarios:

Scalability: A2A tasks could be handled by agents that scale horizontally. For example, one could imagine an A2A “agent” actually being a scalable cloud service with multiple instances behind a load balancer all able to handle tasks/send for the same agent identity. Because tasks have IDs and the protocol is stateless between calls (aside from SSE streams tied to a client connection), a backend service can route or distribute tasks as needed. This could allow an agent service to handle many concurrent requests from many other agents – important in a scenario where, say, a very popular “calendar scheduling agent” is used by hundreds of other personal assistant agents.

In contrast, MCP’s scaling is more about how many servers and tools one agent can juggle. Typically one agent (one user’s session) would not connect to an extremely high number of MCP servers at once – it might select a handful relevant to the user’s request. Each MCP server is relatively lightweight (many are just stateless wrappers around an API). If a particular tool needs to handle heavy load (like many file searches), one could deploy multiple instances of that MCP server and have different agents use different instances, but that’s managed at application level, not by MCP itself.

Performance and Flexibility Considerations

Performance: MCP calls are usually fast, as they are direct function calls or local network calls – adding maybe a few tens of milliseconds overhead to what the raw operation would take. The benefit is that the model doesn’t need to handle large outputs in its context window – e.g., instead of reading a whole PDF in the prompt, the agent can call an MCP tool that returns a summary or relevant section of the PDF. This can dramatically improve the practical performance of an AI solution by focusing the model on just the needed data. Also, MCP allows binary data handling for resources (it can transfer files, images, etc.), which means an agent can, for instance, retrieve an image via MCP and then pass a reference of it to an image analysis model, rather than trying to encode image data in text.

For A2A, the overhead is the HTTP call and JSON serialization, which in most cases is negligible compared to the time the AI spends thinking or the external systems take to act. However, if agents chat back and forth a lot, those milliseconds can add up. It’s expected that agents will have relatively high-level exchanges (not every single thought, but rather larger chunks of reasoning or info). Also, because A2A can stream, the latency for first response can be low – an agent can start transmitting an answer as soon as it begins formulating it, akin to how a large language model streams its answer token by token.

One must also consider failure modes: In MCP, if a tool fails or returns nothing useful, the agent might have to try alternative strategies (if programmed to do so) or ask the user for help. In A2A, if a remote agent fails to complete a task, it will send a failed status. The client agent should be prepared to either retry (maybe with a different agent) or report the failure. Both protocols thus require developers to plan for imperfect outputs, but they make it easier to catch those – MCP with error returns, A2A with task states.

Flexibility and Vendor Lock-in: MCP and A2A both aim to prevent lock-in to a single AI vendor. MCP allows switching out the LLM or switching out the integrated tools (e.g., swap a proprietary database for another, and just change the MCP server implementation). A2A allows mixing agents from different sources – e.g., a company could use a document analysis agent from Vendor X and an email drafting agent from Vendor Y and have them talk. This is particularly useful in business automation, where one vendor might not provide all capabilities. For example, an insurance company might use a specialized underwriting agent from a niche provider, but use a general customer-service agent from a big provider; A2A can let them collaborate on a claim process.

Security and Reliability: Both protocols acknowledge security. MCP ensures that data stays where it should – e.g., an MCP server that reads local files can enforce access controls and never send data externally except to the requesting client. The Anthropic introduction emphasized “securing your data within your infrastructure” as a goal This means an enterprise can deploy MCP servers behind their firewall, connect an AI model to them, and the model can answer questions about internal data without that data ever leaving the premises in raw form. A2A emphasizes secure auth and even mentions support for “enterprise-grade authentication... with parity to OpenAPI’s schemes” – likely meaning API keys, OAuth, etc., plus possible integration with existing identity systems. This allows agents in different orgs to safely work together (if permitted), or to safely prevent unauthorized agents from connecting.

From a reliability standpoint, one could use standard techniques like retries for transient failures in both MCP and A2A. The idempotency of certain operations (like reading a resource, or asking an agent a question) helps – doing it twice usually has the same effect as once (except for side-effectful tools).

Code Snippets for Typical Business Use Cases

To make these concepts concrete, let’s look at how one might use MCP and A2A in code for some business-oriented scenarios. These are simplified examples for illustration:

Example 1: Customer Onboarding with MCP Tools

Imagine an AI agent that automates parts of customer onboarding. It needs to: (a) verify a user’s identity document, (b) create a new record in the CRM database, and (c) send a welcome message on Slack.

Using MCP, we can equip the agent with three tools via servers: one for an ID verification API, one for database access, and one for Slack messaging. Below is pseudocode illustrating how an agent (using an SDK similar to OpenAI’s Agents or a custom client) might set this up and use it:

# Pseudocode: Setting up MCP servers for the agent
from mcp import MCPServerSse, MCPServerStdio

# Connect to an ID Verification API MCP server (remote via HTTP+SSE)
id_verify_server = MCPServerSse(url="https://mcp.mycompany.com/id-verify")

# Connect to a local SQLite database MCP server (running as subprocess via stdio)
db_server = MCPServerStdio(command="python", args=["db_mcp_server.py"])

# Connect to a Slack MCP server (could be remote or local)
slack_server = MCPServerSse(url="https://mcp.mycompany.com/slack")

# Assume agent framework allows adding these servers
agent.add_mcp_server(id_verify_server)
agent.add_mcp_server(db_server)
agent.add_mcp_server(slack_server)

# Now the agent's LLM is aware of tools: e.g., "verify_id", "create_user_record", "send_slack_message"
result = agent.run("""
    Onboard new customer John Doe with ID document 'johndoe_id.png'.
    - Verify the ID.
    - Create their account in the CRM.
    - Send them a Slack welcome message.
""")
print(result)

In this flow, the agent receives an instruction (could be from a user or triggered by an event). Through MCP, it has the following tools available (discovered via tools/list):

When agent.run() executes, the LLM will likely plan something like: First call verify_id on 'johndoe_id.png'. If successful, call create_user_record. Then call send_slack_message. Thanks to MCP, each of those calls is made via call_tool under the hood. The agent’s code waits for each to complete and feeds the results back into the LLM’s context (or the LLM might chain function calls autonomously, depending on the agent framework’s capabilities). Finally, the agent produces a summary or confirmation, which might be printed or sent back to a user interface. This approach cleanly separates the process: the AI handles decision-making and conversation, while MCP servers handle actual data operations with the company’s systems.

Example 2: Document Processing and Analysis with MCP and Vector DB

Consider an agent that can answer questions about company policy documents. The documents are stored as PDFs. A typical RAG (Retrieval-Augmented Generation) pipeline would involve reading PDFs, indexing embeddings in a vector database, and querying that. With MCP, we can modularize this:

A snippet of how an agent might utilize this:

# Connect to a filesystem server and a Qdrant (vector search) server
fs_server = MCPServerStdio(command="npx", args=["-y", "@modelcontextprotocol/server-filesystem", "--root", "/policies"])
vector_server = MCPServerSse(url="http://localhost:8000")  # assume a Qdrant MCP server running

agent.add_mcp_server(fs_server)
agent.add_mcp_server(vector_server)

# Ask a question that requires document retrieval
question = "What is our company policy on remote work and home office setup?"
answer = agent.run(question)

Here’s what happens internally:

This example shows how MCP allows on-demand retrieval and tool use without hardcoding a complex pipeline. The agent builder simply plugged in two servers and the model strategy (possibly via prompt engineering or few-shot examples) handles the rest.

Example 3: Sales Workflow with Multi-Agent (A2A)

For a scenario using A2A, let’s imagine a sales process where multiple specialized agents collaborate:

Using A2A, the Sales Assistant (SA) agent can dynamically delegate tasks. Suppose a customer asks, “Can I get 100 units of product X by next week, and what would it cost?” The SA agent might not have all the info, so it uses A2A to involve the others:

Agent Discovery: The SA agent knows (perhaps via configuration or a registry) the Agent Card URLs for Inventory and Invoicing agents, or it knows how to search for an agent with a certain skill. Let’s assume it has them. The cards would contain info like Inventory Agent’s endpoint and that it has a skill check_stock(product, quantity, date), and Invoicing Agent has generate_quote(product, quantity).

Task delegation using A2A (pseudo-code):

import a2a  # hypothetical library for A2A

# Initialize A2A clients for each remote agent using their Agent Card URLs
inventory_client = a2a.Client(agent_card_url="https://inventory.example.com/.well-known/agent.json")
invoice_client   = a2a.Client(agent_card_url="https://finance.example.com/.well-known/agent.json")

# 1. Check inventory availability by asking Inventory Agent
task_id = "task-001"
request = {
    "taskId": task_id,
    "message": {
        "role": "user",
        "parts": [ { "type": "TextPart", "text": "Check availability of 100 units of Product X by 2025-05-15" } ]
    }
}
response = inventory_client.send_task(request)  # sends tasks/send to Inventory agent

if response.get('state') == 'input-required':
    # The inventory agent might ask something (e.g., "Which warehouse?").
    followup_message = ...  # compose answer as needed
    inventory_client.send_task({
        "taskId": task_id,
        "message": { "role": "user", "parts": [ { "type": "TextPart", "text": followup_message } ] }
    })

# Poll or wait for completion (could also use subscribe for SSE)
status = inventory_client.get_status(task_id)
if status['state'] == 'completed':
    available = status['result']['parts'][0]['text']  # e.g., "Available: 100 units by 2025-05-10 from Warehouse A"
else:
    # handle failure or no availability
    available = "not available"

# 2. Get price quote from Invoicing Agent
quote_task = "task-002"
quote_req = {
    "taskId": quote_task,
    "message": {
        "role": "user",
        "parts": [ { "type": "TextPart", "text": "Generate price quote for 100 units of Product X" } ]
    }
}
quote_response = invoice_client.send_task(quote_req)
quote_status = invoice_client.get_status(quote_task)
price_quote = quote_status['result']['parts'][0]['text']  # e.g., "$5000 with delivery in 7 days"

# 3. Combine results in Sales Assistant's response to customer
if "Available" in available:
    final_answer = f"Yes, we can supply 100 units by next week. {price_quote}."
else:
    final_answer = "Sorry, we cannot fulfill that order by the requested date."

This pseudocode demonstrates an agent orchestrator pattern using A2A. The Sales Assistant agent uses two A2A clients to communicate with the Inventory and Invoicing agents as needed. The actual A2A calls (via send_task and get_status) under the hood construct the proper HTTP requests per the A2A spec (including the JSON structure for messages, etc.). In a real implementation, the SA agent would incorporate this logic into its reasoning: it would ask inventory, maybe wait for a response (perhaps using SSE callbacks rather than polling in a tight loop), then ask finance, then reply to the user.

Notably, all agents remain decoupled:

Example 4: Hybrid MCP + A2A in an IT Support Workflow

To illustrate how MCP and A2A together can form a hybrid architecture, consider an IT support chatbot in a large organization. This chatbot is the frontline agent users interact with. Behind the scenes:

Workflow: A user asks the chatbot, “I cannot connect to the VPN.” The chatbot tries its MCP tools: searches KB articles for “VPN issues” (via a vector search tool), maybe runs a network diagnostics script (via a tool that pings the VPN server). Suppose it finds some info but not enough. It then uses A2A to consult the Network Agent, sending a task: “User cannot connect to VPN. We tried basic diagnostics. What could be the problem?” The Network Agent, which is a separate system (perhaps run by the network team), has skills and data to analyze this. It may ask via A2A for additional info (the chatbot can forward log snippets via artifacts). Ultimately, the Network Agent responds that “There is an outage on VPN server cluster 3, ETA 2 hours to fix.” The chatbot then uses its Slack MCP server to post an update in the IT alerts channel, and finally tells the user the cause and ETA.

This hybrid use shows MCP and A2A are complementary: MCP provides the tools and data integration for individual agent actions, while A2A provides the glue for agent-to-agent collaboration. As one analysis succinctly put it: “A2A is API for agents and MCP is API for tools.” In practice, advanced AI solutions will likely employ both. Google’s ADK example actually demonstrated this: using MCP for tool use and A2A for multi-agent comms in one application.

Early Adopters, Community Insights, and Outlook

Both MCP and A2A are very new (late 2024/early 2025 releases), but they have garnered significant attention:

Looking ahead, the success of these protocols will depend on ecosystem growth. If many tool providers publish MCP servers (the way many services publish APIs today), and many agent providers publish A2A-compatible agents, we could see an “API economy” equivalent for AI agents. Businesses benefit by not having to reinvent connectors or integrations for each AI project – they can leverage standard components. For AI developers and architects, learning MCP and A2A will likely become important, akin to knowing how to call REST APIs.

MCP vs A2A: Summary Comparison

Finally, here is a quick summary comparison of MCP and A2A across key dimensions:

Aspect MCP (Model Context Protocol) A2A (Agent-to-Agent Protocol)
Primary Purpose Connect AI agents to external tools, data, and context at runtime. (MCP) Enable communication between AI agents across orgs. [GitHub] [Koyeb]
Architecture Client–Server model: MCP Client connects to tool-specific MCP Servers. [Docs] Peer model over HTTP: Agents act as Clients & Servers using Agent Cards for discovery.
Communication JSON-RPC 2.0 via stdio/HTTP+SSE. Tool calls are one-off or chained. Notifications supported. JSON-RPC 2.0 over HTTP. tasks/send with streaming and async notifications. Multi-turn dialogue enabled. [Koyeb]
Capabilities Exposed Tools, Resources, Prompts. Defined by server. Used by agent at runtime. Skills listed in Agent Card (e.g., Translate, Analyze). Modalities (text, files, audio) negotiated live.
Agent Memory & Context Injects relevant data via resources. Session memory can use vector DBs, etc. [Examples] Memory is local to each agent. Context is passed through task messages only. No shared state.
Integration Targets LLM apps (assistants, chatbots, IDEs). SDKs for OpenAI, Anthropic, open models. Tool plugins for Slack, Stripe, etc. Multi-agent enterprise ecosystems. CRM, ERP, support bots collaborating. Works in LangChain, Semantic Kernel, etc.
Performance Lightweight. Local or quick calls. Supports streaming for large results. Real-time friendly. Some HTTP overhead. Supports both real-time and long tasks. Allows parallelism across agents.
Reliability & Scaling Failure of one tool doesn’t break session. Scales by adding MCP Servers. Typically single-user agents. Scales across distributed agents. Task IDs enable reconnection. Load-balancing supported. Secure API-like model.
Example Workflows Single-agent: Customer bot uses MCP to fetch purchase history, submit ticket, and respond live. Multi-agent: Sales, Inventory, and Finance agents coordinate on a quote – each with their own domain skill.

As shown, MCP and A2A shine in different but complementary areas. MCP equips an AI agent with the tools and knowledge to act within its environment, making it highly capable in a specific context (business data, APIs, etc.). A2A enables multiple such agents (each potentially MCP-equipped) to form a collaborative team, tackling complex, cross-domain business workflows. Rather than choosing one or the other, organizations can combine MCP and A2A to build powerful agent ecosystems – using MCP for deep integration with their systems and A2A for broad integration across agents and services. Together, these protocols represent a significant step toward scalable, maintainable agent-based business process automation, supported by open standards and a growing community of adopter.

Until the next one,

Tega AdeyemiApril 30, 2025