A2A vs MCP? It Is Quite Complicated

The landscape of artificial intelligence is rapidly shifting from monolithic models towards interconnected ecosystems of specialized agents and tools. As AI systems become more distributed and collaborative, the need for standardized communication protocols becomes paramount. These protocols are not merely technical specifications; they shape how systems interact, who builds what, which tools flourish, and how quickly innovation can propagate through the burgeoning field of agentic AI.

Recently, two prominent protocols have emerged, aiming to bring order to this dynamic space. One protocol, which we’ll refer to as MCP (Model Context Protocol for the purpose of this discussion, though this is not its official name), focuses on standardizing how applications provide context — data and tools — to large language models (LLMs) and AI assistants. Shortly after MCP gained significant traction, another protocol, explicitly named A2A (Agent-to-Agent), was introduced by a major technology company. A2A aims to standardize how autonomous AI agents discover, communicate, and coordinate with each other.

A2A Protocal, Visualized

MCP Protocal, Visualized

The introduction of A2A, positioned by its creators as complementary to MCP, has sparked debate. Are these truly distinct solutions for different layers of the AI stack, destined to work in harmony? Or are they competing visions for orchestrating intelligent systems, potentially leading to fragmentation or a battle for developer adoption? The reality, as we will explore, is complex, rooted in technical nuances, evolving definitions, and the practical dynamics of ecosystem development.

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The MCP Protocols: Standardizing the Flow of Context to AI Models

Before the formal introduction of A2A, MCP was rapidly gaining traction as a de facto standard for enhancing LLM capabilities. Its core objective is elegantly simple yet profoundly impactful: to create a structured, secure, and standardized way for applications to inject relevant, real-time context into an AI model’s operational awareness and allow the model to interact with external functionalities (tools).

Core Architecture and Components:

MCP typically operates using a client-server model, though variations exist:

1. MCP Hosts: These are the primary applications initiating the interaction, often user-facing. Examples include AI-powered Integrated Development Environments (IDEs), specialized desktop assistants, workflow automation platforms, or chat interfaces. The Host orchestrates which MCP Servers are relevant for a given task or query.
2. MCP Servers: These are crucial intermediaries. They act as wrappers or adaptors for specific data sources or tools, exposing them through the MCP interface. A server might provide access to:

• Local Resources: File systems (reading/writing specific files or directories), local databases, currently open application states (e.g., code in an IDE).
• Development Tools: Version control systems (Git status, diffs), build systems, linters.
• External APIs: Project management tools (Jira, Asana), communication platforms (Slack, email), databases, bespoke enterprise systems.
• Structured Data Feeds: Real-time financial data, weather information, etc.

1. MCP Clients: The AI model or agent itself is the primary client. It receives contextual information formatted according to MCP standards from the Host (which sourced it from Servers) and can request actions or further information via the same channel.
2. Data/Tool Sources: The underlying systems being accessed by the MCP Servers.

Technical Functionality:

While MCP itself is more of a specification for data structure and interaction semantics than a rigid network protocol, it implicitly relies on standard data formats and communication patterns:

• Data Format: Payloads are typically structured using JSON, providing a universally understood format for defining tool availability, function signatures (inputs, outputs, descriptions), and the contextual data itself (e.g., file content, API responses).
• Interaction Model: The Host usually mediates. A user query to the Host might trigger the Host to query relevant MCP Servers for context (e.g., “What are the contents of main.py?"). The Host retrieves this data via the MCP Server for the file system and includes it, alongside the user query and potentially definitions of available tools (like a "run linter" tool exposed by another MCP Server), in the payload sent to the MCP Client (the LLM). The LLM's response might include generated text or a request to use one of the advertised tools, which the Host then executes via the appropriate MCP Server.
• Tool Definition: MCP provides a way to describe tools available to the LLM, including their names, descriptions, input parameters (with types), and expected output formats. This allows the LLM to understand how to request an action.
• Security Considerations: While the MCP specification primarily focuses on data structure, secure implementation is vital. Communication between Hosts, Servers, and Clients often relies on underlying secure transport mechanisms (like HTTPS or local IPC secured by the OS). Authentication and authorization for accessing specific tools or data sources are typically handled by the MCP Server implementation or the underlying tool itself, with the Host potentially managing credentials.

MCP’s strength lies in its focused approach to solving the critical “context injection” problem. It enables developers to build more sophisticated, grounded, and capable AI applications by providing a standardized bridge between abstract models and concrete, real-time information and actions.

Protocol A2A: A Framework for Inter-Agent Dialogue and Coordination

A2A enters the scene with a different, albeit related, ambition: to standardize the way autonomous AI agents interact directly with each other. It envisions a landscape where agents, potentially from different providers and built on disparate platforms, can discover one another, negotiate capabilities, exchange information reliably, and coordinate complex, potentially long-running tasks.

Core Architecture and Components (Based on Specification):

A2A defines a more prescriptive protocol, primarily built upon JSON-RPC 2.0, typically transported over HTTP(S).

1. Agent Discovery (AgentCard): The foundation of A2A is discoverability. An agent advertises its existence and capabilities by exposing an AgentCard – a structured JSON document accessible via a known HTTP endpoint. Key fields include:

• name, description: Human-readable identifiers.
• url: The primary endpoint for the agent's JSON-RPC API.
• provider: Information about the agent's creator/host (organization, url).
• version: Semantic versioning for the agent implementation.
• documentationUrl: Link to further documentation.
• capabilities: A crucial sub-object with boolean flags:
• streaming: Indicates support for Server-Sent Events (SSE) for real-time task updates via the tasks/stream method.
• pushNotifications: Indicates support for registering webhook-style callbacks for long-running tasks using the tasks/pushNotification/* methods.
• stateTransitionHistory: Indicates if the agent tracks and can provide the history of a task's state changes.
• authentication: An object detailing the required authentication schemes (e.g., "schemes": ["oauth2", "apiKey"]). The calling agent must support one of these.
• defaultInputModes, defaultOutputModes: Default data types the agent generally handles (e.g., ["text", "file"]).
• skills: An array defining the specific actions the agent can perform.

1. Capability Definition (AgentSkill): This is the heart of what an agent does. Each skill object defines:

• id: A unique machine-readable identifier for the skill (e.g., com.example.weather.getForecast).
• name, description: Human-readable details.
• tags: Optional keywords for categorization/search.
• examples: Sample natural language or structured invocations.
• inputModes, outputModes: Arrays specifying the data formats the skill accepts and produces (e.g., input ["text"] for location, output ["data"] for structured forecast). This allows for multi-modal interactions.

1. Communication Protocol (JSON-RPC 2.0): Interactions occur via standardized JSON-RPC methods sent to the agent’s url:

• tasks/create: Purpose: To initiate a new asynchronous task. Params (CreateTaskParams): Requires skillId, input (a Message object containing parts), optionally pushNotificationConfig, streamingEnabled. Response: Returns a Task object representing the newly created task, including its id and initial status (PENDING or RUNNING).
• tasks/get: Purpose: To poll for the status and result of a task. Params (TaskQueryParams): Requires taskId, optionally includeStateTransitionHistory. Response: Returns the full Task object, including status, output (if completed), error (if failed).
• tasks/send: Purpose: To send additional data or instructions to an ongoing task (facilitating interactive dialogues). Params (SendTaskParams): Requires taskId, input (Message object). Response: Typically acknowledges receipt, potentially updating the task state.
• tasks/cancel: Purpose: To request cancellation of an ongoing task. Params (TaskIdParams): Requires taskId. Response: Returns the Task object, ideally with status transitioning to CANCELLED.
• tasks/stream: Purpose: (If streaming capability is true) Establishes an SSE connection for receiving real-time updates for a specific task. Params (TaskIdParams): Requires taskId. Interaction: Server pushes updates (e.g., intermediate results, state changes) as events.
• tasks/pushNotification/set, /get, /delete: Purpose: (If pushNotifications capability is true) Manages webhook endpoints. Allows a client agent to provide a URL (PushNotificationConfig) where the server agent can send a notification (e.g., a POST request) when a long-running task completes, avoiding the need for continuous polling (tasks/get).

1. Data Exchange (Message, Part, Artifact): Data is encapsulated within Message objects, which have a role ("user" or "agent") and an array of parts. This structure allows for rich, multi-modal content:

• TextPart: Contains a simple text string.
• FilePart: Contains a file object which can hold file bytes (Base64 encoded string), a uri pointing to the file, mimeType, and name. Ensures flexibility for small and large files.
• DataPart: Contains arbitrary structured data as a JSON object.
• Artifact: A more complex structure potentially used for chunked or streamed data transmission, containing parts, an index, append flag, and lastChunk flag.
• metadata: Each Part, Message, and Artifact can include an optional metadata object for custom annotations.

1. Task Lifecycle and State (Task object): A2A defines a clear lifecycle for tasks managed via the protocol. The Task object tracks:

• id: Unique identifier.
• status: The current state (PENDING, RUNNING, COMPLETED, FAILED, CANCELLED).
• skillId: The skill being executed.
• input: The initial input message.
• output: The final output message (if COMPLETED).
• error: Error details (if FAILED).
• stateTransitionHistory: (Optional) A log of status changes.

1. Error Handling: Leverages standard JSON-RPC error codes (32700 Parse error, 32600 Invalid Request, 32601 Method not found, 32602 Invalid Params, 32603 Internal error) and defines A2A-specific errors (e.g., 32001 Task Not Found, 32003 Push Notification Not Supported).

A2A provides a comprehensive, albeit complex, specification designed for robust, asynchronous, potentially long-running interactions between distinct autonomous agents, handling discovery, capability negotiation, multi-modal data exchange, and task lifecycle management.

Structural and Functional Differences: Two Layers or Overlapping Domains?

The designers of A2A explicitly present it as operating at a higher layer than MCP. Let’s analyze this “layered stack” proposition by comparing their structures and functions more directly:

• Focus:
• MCP: Primarily defines the structure of context and tool definitions passed into an agent/model and the expected format for tool invocation requests. It standardizes the what (data/tools available).
• A2A: Primarily defines the protocol for interaction and coordination between agents. It standardizes the how (discovery, messaging sequence, task state management).
• Interaction Pattern:
• MCP: Often involves a central Host mediating between a Client (LLM) and multiple Servers (tools/data). Communication flow is typically Host -> Client (query + context/tools), Client -> Host (response or tool call request), Host -> Server (tool execution), Server -> Host (result), Host -> Client (result).
• A2A: Designed for peer-to-peer interaction (though one agent initiates). Agent A discovers Agent B, calls tasks/create on B, then potentially polls (tasks/get), receives push notifications, or sends further messages (tasks/send). State is managed explicitly via the Task object and defined JSON-RPC methods.
• Discovery & Capabilities:
• MCP: Relies on the Host application’s configuration to know which MCP Servers are available and what tools they offer. Discovery is external to the core protocol specification.
• A2A: Features built-in discovery via the AgentCard and explicit capability negotiation through capabilities flags and detailed AgentSkill definitions including inputModes/outputModes.
• Asynchronicity & State Management:
• MCP: While tool calls can be asynchronous, the core MCP exchange is often request-response oriented from the Host’s perspective. State management for complex workflows is typically the Host’s responsibility.
• A2A: Explicitly designed for asynchronous operations with built-in support for polling (tasks/get), streaming (tasks/stream), and push notifications. Task state (PENDING, RUNNING, etc.) is a core concept managed by the serving agent and queryable via the protocol.
• Data Representation:
• MCP: Focuses on representing tool signatures and contextual data, usually via JSON schemas implicitly agreed upon or defined alongside the tool.
• A2A: Defines specific structures like Message, Part (Text, File, Data), and Artifact to handle diverse data types, including multi-modal content, within the interaction flow.

The Layered View Revisited:

In the ideal layered model, an A2A interaction could trigger underlying MCP interactions. Imagine Agent A (a travel planner) uses A2A to ask Agent B (a flight booking agent) to find_flights (an AgentSkill). Agent B, upon receiving the A2A tasks/create request, might internally use MCP to:

1. Access a real-time flight database (via an MCP Server exposing the database API).
2. Check the user’s saved preferences from a local file (via an MCP Server for the file system).
3. Use a currency conversion tool (via another MCP Server).

Agent B orchestrates these MCP-level tool uses to fulfill the A2A-level find_flights skill request. It then updates the A2A Task status and eventually returns the results via the A2A protocol (e.g., in the output field of the Task object when polled via tasks/get). Here, A2A handles the inter-agent workflow, while MCP handles the agent's interaction with its specific data sources and tools.

The Complications: Blurred Lines, Adoption Hurdles, and Strategic Plays

Despite the logical appeal of the layered model, several factors complicate the picture:

1. The Agent vs. Tool Ambiguity: The most significant challenge. Is a highly specialized API that performs complex analysis (e.g., financial modeling) a “tool” best accessed via MCP, or a specialized “agent” warranting an A2A interface? If it has its own internal state, accepts complex inputs, and performs long-running tasks, it behaves like an A2A agent. If it’s stateless and performs a single function, it looks more like an MCP tool. Many real-world systems fall into a grey area. Developers might choose to wrap the same underlying functionality as either an MCP Server or an A2A agent, leading to inconsistency.
2. Implementation Overhead and Developer Choice: Supporting two distinct protocols adds complexity for developers building both agents and the platforms they run on. Frameworks, SDKs, and tooling need to accommodate both. Developers often gravitate towards the path of least resistance or the standard with the most robust ecosystem support. MCP gained significant early momentum and community investment. A2A, being newer and more complex, faces an uphill battle for adoption, even if technically complementary. The statement “developers can only invest their energy into so many ecosystems” rings true.
3. Potential for Redundancy: Could A2A’s AgentSkill definition, combined with its rich data exchange capabilities (Part types), evolve to become the primary way agents expose all their functionalities, including those that might otherwise be considered "tools"? If Agent B can expose a run_linter skill via A2A, does Agent A still need MCP to access that specific tool on Agent B? This could lead to A2A subsuming some of MCP's functional domain, particularly in agent-to-agent tool usage scenarios.
4. Ecosystem Politics and Strategic Hedging: The simultaneous promotion of A2A alongside the adoption of MCP by A2A’s creators suggests a complex strategy. It acknowledges MCP’s existing foothold while pushing a distinct vision for inter-agent communication where they control the specification. The absence of certain key players from the initial A2A partnership announcements further hints at underlying competitive tensions rather than purely collaborative intent.
5. Tooling and Maturity: MCP, having been around longer and addressing a very concrete initial need, has seen more organic development of supporting tools and integrations within various platforms (IDEs, chat interfaces). A2A, being a more comprehensive specification introduced later, requires significant investment in libraries, SDKs, testing frameworks, and discovery mechanisms (like registries for AgentCards) to reach comparable maturity and ease of use.

The Road Ahead: Scenarios for AI Protocol Evolution

The interplay between A2A and MCP is an unfolding narrative. Several scenarios are plausible:

1. Disciplined Coexistence: The protocols successfully carve out their intended niches. MCP becomes the undisputed standard for connecting an agent/model to its immediately proximate tools and data (local files, integrated IDE tools, direct API wrappers). A2A becomes the standard for higher-level, cross-boundary communication between independent agents, particularly in multi-vendor or complex distributed systems. This requires clear guidelines and community consensus on when to use which protocol.
2. Convergence or Hybridization: The best ideas from both protocols might merge. A future version of one standard could incorporate key features of the other. For instance, A2A might adopt a more structured, MCP-like approach for defining the implementation details within a skill, or MCP might evolve more sophisticated discovery and asynchronous coordination features inspired by A2A. A completely new, unified protocol could even emerge from the lessons learned.
3. Market Competition and Consolidation: Practical factors — ease of implementation, quality of SDKs, dominant platform support, killer applications — could lead developers to favor one protocol significantly over the other. The standard that builds the strongest ecosystem momentum and demonstrates the clearest value proposition might eventually overshadow the other, leading to consolidation, even if the protocols were initially designed to be complementary. Simplicity often wins in standards wars, which could favor MCP for core tool access, while A2A’s complexity might limit its adoption to more niche, high-end coordination scenarios unless its tooling significantly lowers the barrier to entry.

Conclusion: Navigating the Complexities of Agent Communication

The emergence of A2A alongside the established MCP highlights a critical juncture in the development of agentic AI. MCP effectively addresses the fundamental need to provide AI models with access to external context and tools, acting as a vital bridge between the abstract intelligence of LLMs and the concrete world of data and APIs. A2A introduces a comprehensive framework specifically designed for the complex challenge of coordinating interactions between autonomous agents, tackling discovery, asynchronous task management, and multi-modal communication.

While presented as complementary layers — MCP for agent-tool interaction, A2A for agent-agent interaction — the reality is nuanced. The blurry distinction between sophisticated tools and specialized agents, combined with the practical demands on developers and the dynamics of ecosystem adoption, creates significant overlap and potential friction. A2A’s rich feature set for coordination comes at the cost of increased complexity compared to MCP’s more focused approach.

The future relationship between these protocols is uncertain. It will be shaped not just by technical merit but by community adoption, the quality of supporting tools and infrastructure, and the strategic decisions of major platform players. Whether they achieve harmonious coexistence, converge into a unified standard, or engage in a competitive battle for developer mindshare, the outcome will fundamentally influence how we build, deploy, and manage the increasingly interconnected and collaborative AI systems of tomorrow. Understanding the technical underpinnings and strategic implications of both A2A and MCP is crucial for anyone navigating this rapidly evolving landscape.