Protocol Comparison

MCP vs A2A vs Function Calling

A side-by-side comparison of the three foundational protocols for AI integration. Understand the differences, complementary strengths, and when to use each.

FeatureMCPA2AFunction Calling
CreatorAnthropicGoogleOpenAI (industry-wide)
Year202420252023
Primary PurposeConnect models to tools and data sourcesEnable agent-to-agent communicationEnable models to invoke external functions
TransportJSON-RPC 2.0 over stdio or HTTP+SSEHTTP with JSON, SSE for streamingHTTP API (provider-specific)
DirectionModel → Tools (vertical integration)Agent ↔ Agent (horizontal integration)Model → Application → Function
DiscoveryServer capability negotiation at connectionAgent Cards (JSON at well-known URLs)Developer-defined tool schemas in API call
Core AbstractionTools, Resources, PromptsTasks, Messages, ArtifactsFunctions with JSON Schema parameters
Streaming SupportSSE for remote transportSSE for task progress updatesProvider SSE for response streaming
Ecosystem SizeLarge (hundreds of servers, many clients)Growing (early adoption phase)Ubiquitous (all major providers)
ComplexityMedium (protocol + server implementation)Medium-High (agent lifecycle management)Low (JSON Schema + handler)
Best ForConnecting a model to databases, APIs, dev toolsMulti-agent workflows, cross-framework collaborationSimple tool integrations, structured extraction
Open SpecificationYes (open standard)Yes (open standard)De facto standard (provider APIs converging)

When to Use Each Protocol

Use MCP when you need to connect a model to external tools and data

MCP is the right choice when your AI application needs to interact with databases, file systems, APIs, development tools, or cloud services. It provides a standardized, composable way to give models access to external capabilities without building custom integrations for each tool.

Choose MCP when you want your integration to work across multiple AI applications (Claude Desktop, Cursor, custom apps), when you need the security and lifecycle management that the protocol provides, or when you want to contribute a tool to the broader ecosystem that any MCP-compatible application can use.

MCP is less suitable for agent-to-agent communication (use A2A) or for simple one-off function calls that do not need the full protocol infrastructure (use function calling directly).

Use A2A when you need agents to collaborate across boundaries

A2A is the right choice when you have multiple autonomous agents that need to discover each other, delegate tasks, and exchange results -- especially when those agents are built on different frameworks, powered by different models, or operated by different teams or organizations.

Choose A2A when building multi-agent workflows with specialized roles (research, coding, review), when enabling cross-organization agent collaboration, or when building agent marketplaces where diverse agents need a common communication protocol.

A2A is less suitable for connecting a single model to its tools (use MCP) or for basic API integrations that do not involve autonomous agent behavior (use function calling).

Use Function Calling for direct, simple tool integrations

Function calling is the right choice for straightforward integrations where a model needs to call specific functions -- looking up data, performing calculations, making API requests, or extracting structured data from text. It is the simplest path from natural language to programmatic action.

Choose function calling when you are building within a single application context, when the set of tools is small and well-defined, when you need structured data extraction (treating the output schema as a "function"), or when you are prototyping and want the fastest path to tool use.

Function calling is less suitable when you need cross-application tool sharing (use MCP) or when you need autonomous agent collaboration (use A2A). However, function calling is the underlying mechanism that both MCP and A2A build upon -- understanding it is essential regardless of which higher-level protocol you use.

How They Work Together

These three protocols are not competitors -- they are complementary layers of an emerging AI protocol stack. Function calling is the foundational mechanism: the way models express intent to invoke tools. MCP builds on this by standardizing how tools are discovered, described, and managed across applications. A2A builds on top of both by enabling the autonomous agents that use MCP-connected tools to collaborate with each other.

In a fully realized system, an AI agent might use function calling internally to invoke tools, connect to those tools through MCP servers (a database server, a code analysis server, a search server), and coordinate with other specialized agents through A2A (delegating research to a research agent, code generation to a coding agent).

The key insight is choosing the right level of abstraction for your needs. Start with function calling for simple integrations. Adopt MCP when you need standardized, portable tool connectivity. Adopt A2A when you need multi-agent orchestration. Many production systems will use all three protocols simultaneously, each at its appropriate layer.