A2A vs MCP: How Google and Anthropic Protocols Compare

The Model Context Protocol (MCP), introduced by Anthropic in late 2024, standardizes how AI models connect to external tools and data sources. Before MCP, every integration required custom code. Developers built specific connectors for each database, API, and file system. This created technical debt and prevented tool sharing across model providers.

MCP works like a USB port for AI. Once a tool exposes an MCP server, any compatible model can use it immediately. The ecosystem has grown to over 10,000 community-built servers covering everything from GitHub and Slack to PostgreSQL and AWS services. In December 2025, Anthropic donated MCP to the Agentic AI Foundation (AAIF) under the Linux Foundation, with co-founders including OpenAI and Block.

MCP provides three core primitives:

Tools: Functions agents can invoke, like "query_database" or "create_calendar_event." Tools have typed inputs and outputs, making them predictable for automation.

Resources: Read-only data sources providing context. Agents browse and retrieve files, records, or API responses as needed.

Prompts: Pre-defined templates helping agents interact with specific systems efficiently.

MCP focuses on single-agent capabilities. It connects one model to its tools but does not handle coordination between multiple agents. That is where A2A comes in.

The Agent-to-Agent (A2A) protocol, launched by Google in April 2025, solves a different problem: how autonomous agents communicate and coordinate. Most enterprise AI systems use multiple specialized agents, often built on different frameworks like LangChain, CrewAI, or AutoGen. A2A lets these agents discover each other, delegate tasks, and collaborate.

Google launched A2A with over 50 enterprise partners including Atlassian, Salesforce, and PayPal. By July 2025, version 0.3 added gRPC support, signed security cards, and extended Python SDK capabilities. The protocol now counts over 150 supported organizations and lives under the Linux Foundation as an open-source project. Google explicitly designed A2A to complement MCP, not compete with it.

A2A provides four key capabilities:

Agent Cards: Machine-readable JSON metadata describing what an agent does, its endpoints, and requirements. Client agents use these cards to discover and evaluate remote agents before delegating tasks.

Task Lifecycle: Communication between agents centers on "task" objects with defined lifecycles. Tasks can complete immediately or run asynchronously, with both agents staying in sync on status updates.

Secure Transport: A2A runs over HTTPS with JSON-RPC 2.0 for data exchange. Authentication supports OpenAPI-aligned security schemes including API keys, OAuth 2.0, and OpenID Connect.

Asynchronous Collaboration: A2A supports long-running workflows where agents work independently and report back. Think of it like parallel workstreams with periodic sync points.

A2A is the orchestration layer for multi-agent systems. It replaces hard-coded handoffs between system components. An A2A-compatible research agent can work with any A2A-compatible writing agent, regardless of who built them or what models they use.

These protocols are not competing standards. They solve different problems in the same stack. MCP is about tool calling and data access. A2A is about communication and coordination between autonomous agents.

MCP gives agents access to tools. A2A gives agents access to teammates. Production systems typically use both together.

Production systems typically use both protocols together. A2A handles coordination between agents while MCP provides tool access. Keeping these concerns separate makes systems easier to maintain.

Consider a marketing workflow:

1. An Orchestrator Agent receives a campaign request
2. The Orchestrator uses A2A to delegate to an SEO Analysis Agent and a Copywriting Agent
3. The SEO Agent uses MCP to query search volume data and scan the website
4. Results pass back to the Orchestrator via A2A
5. The Orchestrator delegates copywriting to the Copywriter Agent via A2A
6. The Copywriter uses MCP to save drafts to workspace storage

This setup is modular. You can swap out the copywriter agent by changing an A2A reference. You can switch storage providers by updating MCP server configuration. The protocols separate "who does the work" from "what tools they use."

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Most A2A vs MCP discussions skip an important question: how do agents actually exchange files and artifacts? Both protocols handle metadata and control flow, but when agent A generates a report that agent B needs to review, where does that file live?

This is where persistent storage layers come in. Fast.io works as a bridge that connects both protocols:

For A2A: When agents exchange artifacts, they typically share references (URLs) rather than file contents. Fast.io provides secure, shareable links that agents can pass through A2A messages. The recipient agent can then access the file directly via Fast.io's API or MCP server.

For MCP: Fast.io's MCP server provides 251 tools for file operations. Agents can upload, download, search, and manage workspace files through standard MCP interfaces. Files stored in Fast.io are immediately available to any MCP-compatible agent.

The Combined Workflow:

1. Agent A (research specialist) writes a report and uploads it to Fast.io
2. Agent A sends an A2A message to Agent B (review specialist) with a reference to the file
3. Agent B receives the message and retrieves the file via Fast.io's MCP server
4. Agent B reviews the report and leaves comments via Fast.io's collaborative features
5. Both agents have full audit trails of who accessed what and when

This pattern (A2A for coordination, MCP for capabilities, Fast.io for persistence) solves the "where does the data go?" problem that most multi-agent architectures run into.

Fast.io's agent features include:

• Intelligence Mode: Auto-indexes files for RAG when enabled, making content searchable by meaning
• Ownership Transfer: Agents can build workspaces and hand them off to human users
• Webhooks: Real-time notifications when files change, enabling reactive workflows
• File Locks: Prevents conflicts when multiple agents access the same file

The free agent tier provides 50GB storage and 5,000 monthly credits. No credit card is required. Agents sign up the same way humans do. See our pricing page for details.

Early adoption data backs up the case for both protocols:

Developer Productivity: Teams using MCP report faster integration times. Once an MCP server exists for a database or API, any agent can access it without custom code. The time savings grow as you add more agents that reuse the same servers.

Performance: Fast.io benchmarks show MCP sessions using Streamable HTTP or SSE achieve 50-60% faster response times compared to legacy polling-based approaches. Lower latency matters for real-time agent applications.

Ecosystem Growth: The MCP server ecosystem has grown to over 10,000 community-built servers since late 2024. A2A launched with 50+ enterprise partners and now counts over 150 supported organizations, including major cloud providers and SaaS vendors. Both protocols are now governed under the Linux Foundation.

Tool Adoption: Production agent systems routinely integrate 50-200+ MCP tools. Fast.io's official MCP server exposes 251 workspace operations, from file uploads to RAG queries.

The trend is clear: custom agent integrations are giving way to modular, reusable components. Standardized protocols cut development time and reduce maintenance work as agent systems grow.

Here are a few concrete scenarios showing how these protocols work together.

Enterprise Research Assistant

A consulting firm builds a research system with three agents:

• Research Agent: Uses MCP to query databases, search documents, and gather information
• Analysis Agent: Processes findings and generates insights
• Review Agent: Checks output quality and formatting

The workflow uses A2A for coordination. The research agent delegates analysis tasks to the analysis agent, which then hands off to the review agent. All three use MCP to access Fast.io for file storage, with reports uploaded and shared via the workspace.

Client Portal Builder

An agency creates an agent that builds branded client portals:

• The agent uses Fast.io's API to create workspaces, upload assets, and configure sharing
• When the portal is ready, the agent uses A2A to notify the client's systems (if they also support A2A)
• The agent transfers ownership of the workspace to the human client
• The client receives a fully configured data room without manual setup

Multi-Agent Code Review

A development team uses agents for code review:

• A security specialist agent scans for vulnerabilities via MCP tools
• A style guide agent checks formatting via MCP
• A logic agent reviews algorithmic complexity via MCP
• These agents coordinate via A2A to produce a unified review report
• The report is stored in Fast.io and linked to the pull request

Both protocols are still evolving. Knowing the current constraints helps you plan around them.

A2A Limitations:

• Growing quickly (150+ organizations), but many implementations are still early-stage
• Agent discovery relies on agent cards, which require manual configuration in most deployments
• Error handling for partial failures in multi-agent workflows is complex
• Cross-organizational agent trust is still an open problem

MCP Limitations:

• The 10,000+ community servers vary in quality and maintenance
• No standardized authentication beyond transport-level security
• Complex enterprise integrations (SAP, Salesforce, custom internal tools) may still require custom work
• Server configuration and deployment remain developer tasks

Integration Challenges:

• Most organizations will need both protocols, which adds architectural complexity
• Monitoring and debugging distributed agent workflows requires new tooling
• Version management for agent capabilities and tool schemas adds overhead

The protocol designs are solid, and both have strong backing from Google, Anthropic, and the Linux Foundation. The main risk is implementation maturity, not the protocol architecture itself.