Interoperability becomes important when an agent must use capabilities owned by another application or delegate work to an independently operated agent. The problem is not simply moving JSON. Each side needs a shared contract for discovery, inputs, outputs, lifecycle, authorization, failures, and version changes.
Model Context Protocol, or MCP, connects an AI host to servers that expose tools, resources, and prompts. Agent2Agent, or A2A, describes communication with a remote agent that accepts messages, manages tasks, and returns artifacts. A direct API or queue can still be the better choice when both sides are internal and the contract is already stable.
Protocols create consistency; they do not create trust. The caller must still authenticate the remote system, authorize each capability for the current user, validate every payload, protect credentials, and record what crossed the boundary.
| Boundary | Best Starting Point | Reason |
|---|---|---|
| Model needs a tool or data source | MCP or a typed local tool | The host discovers and invokes bounded capabilities. |
| One agent delegates a task to another | A2A or an application task API | The remote system owns a task lifecycle and returns an artifact. |
| Known internal service call | Direct API | A protocol adapter may add no useful independence. |
| Long-running internal work | Queue plus durable workflow | Delivery and recovery matter more than agent discovery. |
In MCP, a host application creates a client connection to a server. The server can advertise tools for actions, resources for context, and prompts for reusable interaction templates. Initialization negotiates protocol versions and capabilities before the host uses them.
The host remains responsible for the user experience and policy. Discovering a tool does not mean every user may call it. A production host should filter tools by identity and tenant, present consent for sensitive calls, validate results, and keep server credentials outside model context.
Use the dedicated MCP tutorial for implementation details. This lesson focuses on where MCP belongs in a larger agent architecture and where it does not.
A2A treats the remote agent as an independent service. An Agent Card describes its identity, endpoint, skills, supported interfaces, and authentication requirements. A client can send a message and receive either a direct message or a stateful task for longer work.
Tasks have identifiers and lifecycle states. Results belong in artifacts, while messages carry instructions, clarification, or status. This distinction matters because a chat transcript is not a reliable substitute for a durable deliverable.
An Agent Card is metadata, not proof that an agent is safe. Use trusted registries or configured endpoints, verify transport security and signatures when available, and apply a local allowlist before delegation.
MCP usually exposes the building blocks an agent may use. A2A exposes another agent that owns how a delegated task is completed. A remote A2A agent may itself use MCP servers, direct APIs, databases, or human review behind its boundary.
Avoid layering both protocols into a simple application merely because they are available. Add a protocol when independent ownership, reuse across hosts, capability discovery, or a portable task contract pays for the additional operational surface.
| Question | MCP | A2A |
|---|---|---|
| What is exposed? | Tools, resources, and prompts | Agent skills and task processing |
| Who controls orchestration? | The host application | The remote agent for delegated work |
| Typical result | Tool result or contextual content | Message, task status, or artifact |
| Primary design concern | Capability and context boundary | Delegation and task lifecycle boundary |
A downstream server needs enough trusted identity to authorize the request, but it should not receive a broad copy of the user session. Exchange short-lived, audience-bound credentials or a signed delegation claim with the smallest required scopes.
Never place bearer tokens, API keys, or reusable credentials inside prompts, messages, artifacts, or Agent Cards. Model-visible text can be logged, replayed, summarized, or exposed to untrusted content. Credential acquisition and refresh belong in trusted runtime code.
Multi-tenant systems must bind every request to the authenticated tenant and verify that returned artifacts belong to the same task and principal. Names and natural-language claims are not authorization evidence.
Protocol compatibility is only the first layer. Tool schemas, Agent Cards, skill descriptions, artifact formats, and policy behavior also change. Cache discovery metadata with bounded freshness, react to capability updates, and reject silent downgrades that remove a required feature.
Define timeouts, cancellation, duplicate delivery, partial artifacts, expired tasks, unsupported operations, and remote unavailability before launch. A caller should know whether to retry, ask for input, select another provider, or return partial progress.
Contract tests should run against every supported server version. Record the negotiated protocol, advertised capability set, schema digest, and remote agent version in traces so an incident can be reproduced.
For each external boundary, write down the owner, trust level, user identity flow, protocol, capabilities, version policy, timeout, retry rule, artifact schema, and fallback. This short record prevents a protocol choice from hiding unresolved security and operations decisions.
Revisit the record when a connector gains write access, an agent begins serving more tenants, or a previously synchronous task becomes long-running. Those changes alter the threat model even if the endpoint stays the same.
Use MCP to expose tools, resources, and prompts from capability providers to a host. Use an agent-to-agent protocol when independent agent services exchange task ownership, status, messages, and artifacts. A gateway may support both, but translating a tool call into a delegated task changes identity, lifecycle, cancellation, and audit semantics.
Negotiate capabilities and versions rather than assuming every peer supports every feature. Preserve caller identity, tenant, scopes, task ID, trace context, artifact provenance, and expiry across boundaries. Never forward a bearer token to a different audience or let a remote agent claim that a user approved an action.
Design failure and discovery policy before federation. A peer can disappear, change capabilities, return an unsupported artifact, request more authority, or complete after cancellation. Bound retries, authenticate metadata, isolate untrusted results, and keep a local owner responsible for the final user-visible outcome.
This decision function keeps protocol selection tied to ownership and lifecycle needs instead of popularity.
from dataclasses import dataclass
@dataclass(frozen=True)
class Boundary:
independent_owner: bool
remote_agent_owns_plan: bool
capability_discovery: bool
long_running: bool
def choose_integration(boundary: Boundary) -> str:
if boundary.remote_agent_owns_plan:
return "A2A_OR_TASK_API"
if boundary.capability_discovery and boundary.independent_owner:
return "MCP"
if boundary.long_running:
return "QUEUE_AND_DURABLE_WORKFLOW"
return "DIRECT_TYPED_API"
cases = [
Boundary(True, False, True, False),
Boundary(True, True, True, True),
Boundary(False, False, False, False),
]
for case in cases:
print(choose_integration(case))MCP
A2A_OR_TASK_API
DIRECT_TYPED_APIThe conditions are intentionally conservative: independent ownership alone does not force a new protocol.
Stateful delegation needs a lifecycle that rejects impossible transitions and late updates.
ALLOWED = {
"submitted": {"working", "rejected", "cancelled"},
"working": {"input_required", "completed", "failed", "cancelled"},
"input_required": {"working", "cancelled"},
"completed": set(),
"failed": set(),
"rejected": set(),
"cancelled": set(),
}
def transition(current: str, requested: str) -> str:
if requested not in ALLOWED[current]:
raise ValueError(f"Invalid transition: {current} -> {requested}")
return requested
state = "submitted"
for requested in ["working", "input_required", "working", "completed"]:
state = transition(state, requested)
print(state)working
input_required
working
completedTerminal states accept no later status, which protects callers from stale or contradictory updates.
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They address different boundaries. MCP exposes capabilities and context to a host; A2A lets a client communicate with a remote agent that owns task execution. A system may use either, both, or neither.
No. A local function or direct API is simpler when reuse and independent ownership are absent. MCP is valuable when a stable capability boundary needs to work across compatible hosts.
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