MCP vs A2A: How AI Agent Protocols Work Together

MCP and A2A solve different interoperability problems in agentic AI. The Model Context Protocol (MCP) connects an AI application to tools, resources and prompts. Agent2Agent (A2A) connects one agent to another for discovery, communication and task delegation. They are complementary layers, not competing replacements.

MCP can expose a CRM search, database query or deployment tool to an agent. A2A can let a support agent delegate a billing question to a specialist agent. Neither protocol guarantees reliable decisions or safe permissions: architecture, evaluation and operational controls still matter.

MCP and A2A in one sentence

MCP connects agents to capabilities: tools, data and reusable prompts.
A2A connects agents to collaborators: agents, tasks, messages and results.

A useful mental model is an operating system. MCP is closer to the interface between an application and its tools. A2A is closer to the interface between applications that need to cooperate. A real workflow can use both: an orchestrator communicates with specialist agents through A2A, while each specialist uses MCP to access approved systems.

What MCP solves

An agent becomes more useful when it can look up an order, inspect a repository, retrieve a policy document or call a business API. MCP provides a common protocol surface for exposing those capabilities to an AI application.

The boundary is explicit: the model decides whether a capability is relevant; the MCP server defines what it does and which inputs it accepts; the host application controls the session and permissions. This makes integration more structured, but does not eliminate risk.

The MCP specification released on July 28, 2026, moved the protocol core toward a stateless request/response model. The release also introduced Multi Round-Trip Requests for interactions such as elicitation, sampling and roots/list, added cache hints for list and resource responses, formalized extensions and documented authorization changes.

MCP fits when one agent needs controlled access to internal data, business APIs, developer tools or actions requiring schemas, authentication and audit logs. It is not primarily a protocol for asking another independent agent to own a task.

What A2A solves

Many workflows are too broad for one agent. A research agent may need a data-analysis agent; a travel-planning agent may need a booking agent; an IT agent may need a security agent. A2A addresses communication between those separate agents.

The calling agent does not need to know which model, framework or tools power the specialist. It needs a contract for discovery, communication, task progress and results. The specialist can remain autonomous behind that boundary.

In an April 9, 2026 press release, the Linux Foundation described A2A 1.0 as the project’s first stable specification and reported support from more than 150 organizations, plus active production deployments across supply chain, financial services, insurance and IT operations. These figures are reported by the Linux Foundation and are not an independent audit of production quality.

A2A fits when agents use different runtimes, belong to different teams or need explicit identity, task status and result handoff. It does not replace the tool-access layer inside each agent.

MCP vs A2A: the practical difference

Question	MCP	A2A
What is connected?	An AI application and external capabilities	One agent and another agent
Main abstraction	Tools, resources and prompts	Agents, tasks, messages and results
Typical action	Agent calls a tool or reads a resource	Agent delegates or collaborates
Main concern	Permissioned capability access	Identity, discovery and task lifecycle
Example	Agent queries a CRM through an MCP server	Support agent asks a billing agent to resolve an invoice
Does it guarantee good decisions?	No	No

The decision rule is simple: if the agent needs to use something, evaluate MCP. If it needs to ask another agent to do something, evaluate A2A. If both are true, a layered design may be appropriate.

How they work together

Consider a retailer whose support orchestrator receives a question about a delayed international order.

The orchestrator identifies that logistics expertise is needed.
It selects a logistics specialist through an A2A-compatible interface.
It sends a scoped task with the order reference and minimum required context.
The logistics agent uses MCP to query the order system, carrier APIs and delivery policy.
It returns a structured explanation and evidence.
The orchestrator answers, asks for clarification or escalates to a human.

This separation can reduce permission scope: the support agent does not need every carrier API, and the logistics agent does not need the customer’s entire account. Each boundary can have its own authentication, rate limits, logging and evaluation.

It also creates failure modes. An agent may be unavailable, a tool may return stale data or a handoff may omit context. Production designs need timeouts, bounded retries, idempotency, output validation, human escalation and a policy for partial results.

Security boundaries matter more than protocol choice

Before connecting either protocol, define:

Identity: Which user, service or agent is making the request?
Authority: What may it read, change or delegate?
Data minimization: What is the smallest context needed?
Consent: Which actions require user confirmation?
Isolation: Can an untrusted tool or agent reach unrelated systems?
Auditability: Can the team reconstruct what was requested and returned?
Revocation: Can access be removed without redeploying everything?

The MCP announcement documents authorization hardening, including issuer validation related to RFC 9207 and a move away from Dynamic Client Registration toward Client ID Metadata Documents. The Linux Foundation says A2A 1.0 includes modernized security flows and Signed Agent Cards for cryptographic identity verification. These features are building blocks, not substitutes for a threat model.

Evaluate the system, not only the model

Agentic systems can fail even when the model appears capable. A tool description may be ambiguous, an agent may call too many tools or a handoff may loop.

Anthropic’s guidance on writing tools for agents recommends testing with real tasks and evaluating accuracy, runtime, tool-call count, token consumption and tool errors. For a multi-agent workflow, track:

task success and human-correction rates;
latency, token and infrastructure cost;
MCP calls and A2A handoffs per task;
permission denials and policy violations;
stale, malformed or contradictory results;
escalation rate and reasons.

Start with a narrow task whose outcome can be checked. Compare a single-agent baseline with the layered design. If MCP/A2A adds latency and failure points without improving outcomes, the architecture is more complex than the problem requires.

Which protocol should a small team adopt first?

Adopt MCP first when the immediate need is safe, observable access to a small set of business tools. Evaluate A2A first when independent specialist agents already exist and the main pain point is delegation across boundaries. Use both only when the benefits are measurable: narrower permissions, reusable capabilities, clearer ownership or better task quality.

Production checklist

Define each tool and agent’s capability narrowly.
Validate inputs and outputs with strict schemas.
Document authentication, authorization and consent.
Minimize context before every call and handoff.
Test timeouts, bounded retries, idempotency and fallbacks.
Trace every call and result to a user, agent and task.
Test normal, ambiguous, adversarial and unavailable-tool cases.
Enforce cost and latency budgets.
Provide human escalation for high-impact actions.
Pin protocol and SDK versions before rollout.

FAQ

Is A2A a replacement for MCP?

No. MCP connects an AI application to tools, resources and prompts. A2A connects agents for communication and delegation. A system can use A2A between agents and MCP inside each agent.

Can MCP and A2A be used together?

Yes. An orchestrator can delegate through A2A while a specialist uses MCP to access a database and APIs. Give the interfaces separate permissions and observability.

Does either protocol make agents reliable by itself?

No. Reliability comes from validated schemas, restricted permissions, representative evaluations, controlled retries, monitoring and human escalation.

What should a small team build first?

Start with the smallest workflow with a verifiable outcome. Prototype MCP for tool access or A2A for agent delegation, and keep a single-agent baseline for comparison.

Sources

If you are evaluating an agent workflow, explore the SignorCrypto Toolkit for practical tools and implementation starting points.