MCP vs A2A: what AI agent teams need for tool access

At a glance

What this is: This is a comparison of MCP and A2A that shows why one protocol governs tool access while the other governs agent collaboration.

Why it matters: It matters because IAM teams now have to separate tool authorisation, consent, and delegation decisions across agentic systems instead of treating all agent communication as the same control problem.

Context

MCP and A2A solve different governance problems. MCP standardises how an agent reaches tools and data sources, while A2A standardises how agents find each other and hand work across a delegation chain.

For identity teams, that split matters because it changes where access decisions live. Tool permissions, consent, and authentication policies belong with MCP-style execution paths, while peer discovery and task handoff introduce a separate delegation layer that needs its own controls.

Most enterprise agentic systems will not stay neatly on one side of that boundary. The practical question is not which protocol is better, but how to prevent orchestration from outrunning authorisation.

Key questions

Q: Where does MCP fail in agentic systems that need collaboration between multiple agents?

A: MCP fails when the problem is not tool access but peer coordination. It can tell an agent what tools exist and how to call them, but it does not define discovery, negotiation, or task handoff between agents. Once collaboration becomes the requirement, a separate delegation protocol is needed.

Q: Why do AI agent teams need to separate orchestration from tool authorisation?

A: Because the agent that receives work is not always the same identity that executes it. Orchestration decides who does what, but tool authorisation decides what systems that work can touch. If those layers are merged, an agent can inherit too much access simply by being part of the workflow.

Q: When does agent delegation become an access-control problem?

A: Delegation becomes an access-control problem the moment an agent can act beyond the original human request or create another actor with inherited authority. At that point, the security team must control depth, scope, and duration of the delegation chain. If those limits are absent, the agent can amplify privileges faster than traditional IAM reviews can catch.

Q: How should security teams separate MCP from A2A in an agent architecture?

A: Security teams should treat MCP as the execution layer and A2A as the coordination layer. MCP standardizes how an agent discovers and calls tools, while A2A standardizes how agents find each other and delegate work. Keep the tool layer strictly separate so coordination choices can change later without forcing a rewrite of every integration or access path.

Technical breakdown

MCP as the tool-access layer for agents

MCP uses a client-server model built on JSON-RPC so an agent can discover registered tools, call them in a predictable way, and parse structured responses. That gives developers a standard interface for databases, files, APIs, and other external systems without hard-coding a new connector for every integration. The security implication is that the protocol defines execution access to resources, not peer negotiation between agents. When agents can invoke tools dynamically, the control boundary shifts to permissions, consent, and scoped authorisation on the MCP server side.

Practical implication: Treat each MCP server as an access-controlled execution surface, not just an integration endpoint.

A2A as the delegation layer between agents

A2A is designed for agent-to-agent discovery, communication, and coordination. Agents publish capabilities through Agent Cards, then negotiate tasks across a peer-to-peer style workflow rather than through a single master process. That makes A2A useful for orchestration, but it does not define structured tool access, long-running I/O contracts, or fine-grained permissions. In practice, the protocol moves governance into the delegation path: who can discover whom, what task can be handed off, and what consent or authentication must exist before one agent relies on another.

Practical implication: Govern discovery and handoff separately from the downstream tools those agents may later use.

Why most agentic systems need both protocols

A2A covers orchestration while MCP covers execution, so neither protocol fully replaces the other in enterprise workflows. If a top-level agent delegates work to specialist agents, A2A handles the task split, but each specialist still needs a governed way to reach databases, APIs, or internal services through MCP. The result is a two-layer trust model: one for agent collaboration and one for tool access. That duality is useful, but it also creates an identity boundary that security teams must model explicitly instead of assuming one control plane covers both.

Practical implication: Map orchestration and tool access to separate policy decisions before agents are allowed into production.

NHI Mgmt Group analysis

Tool access and agent delegation are separate identity problems: MCP governs what an agent can touch, while A2A governs who can hand work to whom. Those are not interchangeable control planes, even if both use similar transport concepts. The practical implication is that teams that collapse them into one policy layer will miss where authorisation actually changes hands.

The trust boundary moves when peer-to-peer coordination enters the design: A2A introduces discovery and task handoff, which means the system now depends on identity assertions about another agent, not just a tool endpoint. That is a different governance problem from API access, and it requires explicit consent and delegation rules. Practitioners should treat inter-agent trust as a first-class design decision.

MCP access control is the execution boundary for non-human identities: once an agent can query files, databases, or APIs through MCP, the security model resembles NHI governance more than simple integration management. That means least privilege, scope control, and auditability matter at the point of invocation, not only at deployment time. The practitioner takeaway is to govern each tool call as an access event.

Identity orchestration gap: the article exposes a governance gap where orchestration and execution are treated as one problem, even though they fail differently. A2A can route work, but it cannot by itself enforce the permissions behind the work; MCP can reach systems, but it cannot define which agent should be trusted to ask. Teams need to model both layers before the first production deployment.

Protocol convergence will not remove accountability: combining MCP and A2A makes agentic systems more capable, but it also increases the number of places where identity decisions can drift out of policy. The more capable the system becomes, the more important it is to assign clear ownership for tool authorisation, delegation policy, and audit review. Practitioners should expect governance complexity to rise, not fall.

From our research library:

• 24,008 unique secrets were exposed in MCP configuration files in 2025 alone, the protocol's first year of widespread adoption, according to the State of Secrets Sprawl 2026.
• Read next: AI Agent Identity Security Buyer's Guide

What this signals

Identity orchestration gap: many teams will be tempted to treat agent-to-agent messaging as a general extension of API integration, but that is a category error. Once A2A enters the stack, identity moves from request-level access to delegation-level trust, and that requires a separate control model.

The most important programme shift is to model MCP and A2A as two different security boundaries. One governs what a non-human identity can execute, the other governs who may delegate work to that identity, and both need auditability before agentic systems scale.

For practitioners

• Define separate trust policies for orchestration and execution Write one policy set for agent-to-agent handoff and another for tool access through MCP servers. Keep delegation approval, consent requirements, and downstream permissions distinct so an agent cannot inherit access simply because it was delegated work.
• Inventory every MCP server as a governed access surface Record which tools, data sources, and APIs each MCP server exposes, then classify the permissions attached to each one. Review those surfaces as if they were production service accounts with scoped access rather than informal developer integrations.
• Control Agent Cards and discovery rules Limit which agents can advertise capabilities, which peers can discover them, and which tasks can be handed off without additional approval. Discovery is part of the trust boundary, not a neutral directory function.
• Log delegation and invocation separately Capture one audit trail for task handoff between agents and another for tool calls made after delegation. That separation helps you see whether a failure came from the orchestration layer or from the execution layer.

Key takeaways

• MCP and A2A are not competing standards but different governance layers, with one governing tool access and the other governing agent collaboration.
• The practical risk is boundary confusion, where delegation pathways and execution permissions are treated as if they were the same control surface.
• Teams should model discovery, handoff, and tool invocation separately so agentic systems do not inherit unintended access through orchestration.

Key terms

• MCP: Model Context Protocol, an open way for AI agents to connect to tools and data sources. It improves interoperability, but it also introduces a shared integration layer that must be governed carefully because the protocol can widen access across many systems at once.
• A2A: A2A, or agent-to-agent communication, describes interactions where one AI agent delegates work to another agent. These flows can be stateful, multi-step, and policy sensitive, so organisations need authentication, tracing, and guardrails to prevent uncontrolled delegation, unsafe data sharing, and opaque decision chains.
• Agent Card: An Agent Card is a machine-readable description of an agent’s capabilities, endpoint, and invocation details. In A2A-style systems it helps other agents discover and call the agent at runtime, which makes the card a governance object as much as a technical descriptor.
• Tool Access Boundary: A tool access boundary is the limit around what external systems an AI agent may query or control. It matters because every integration can expand the agent's privileges, expose sensitive data, and increase the chance of unintended actions if permissions are not tightly scoped.

Deepen your knowledge

NHI governance, agentic AI identity, and machine identity lifecycle are core topics in our NHI Foundation Level course, the industry's only accredited NHI security programme. If you are building or maturing an IAM programme, it is worth exploring.