MCP vs A2A: Tool Access and Agent Delegation Need Different Controls

What is the difference between MCP and A2A?

Model Context Protocol, or MCP, gives AI applications a standard way to use external tools, resources and prompts. A coding assistant might use an MCP server to read project documentation or create an issue. The central buyer question is what capabilities the application can reach. See the MCP specification.

Agent2Agent, or A2A, standardizes communication between agents. An agent can request work from another agent and receive messages, task updates and results. The receiving agent can keep its own tools and implementation private. The central buyer question is who receives the work and what authority comes with it. See the A2A specification.

These are different integration relationships, not competing security products. Neither protocol makes a connected service trustworthy simply because it speaks the standard.

What security questions does MCP raise?

Start with the tools and data exposed through each server. A document search tool and a tool that changes production settings need different permissions. Review the server owner, the credentials it uses and the actions available to each caller. Apply least privilege to the underlying systems as well as the AI application.

Treat tool descriptions and returned content as potential sources of untrusted instructions. A useful result can contain text that tries to redirect the agent. Review how the application separates retrieved content from authorized instructions, and require approval for actions with serious consequences.

Ask vendors to demonstrate a denied action, not only a successful connection. Can an agent read an approved repository while being denied access to another? Can it propose a change without being allowed to publish it? Those tests make MCP security concrete.

What security questions does A2A raise?

A2A adds a delegation boundary. Before sending work, verify the receiving agent's identity, owner and approved purpose. Its advertised capabilities help with discovery, but a capability description is not permission to receive confidential data or act for an employee.

Define the authority attached to each request. May the receiving agent only analyze information, or may it change a business record? May it involve another agent? Do not assume the original user's permissions or approval automatically carry across every handoff. AI agent delegation needs explicit limits.

Keep task results subject to review. A response from an authenticated agent can still contain an error or unsafe instructions. Ask how the system restricts access to task history, validates returned work and records responsibility. A completed task should leave enough evidence to explain who requested it and which actions followed.

How would an enterprise use MCP and A2A together?

Consider a procurement assistant preparing a supplier renewal. It uses MCP to retrieve the contract and read the supplier record. It then uses A2A to ask a legal agent for a review. The legal agent may use its own MCP connections to consult approved policy documents.

The security boundaries differ at each step. Contract access should follow the requester's permissions. The legal agent should receive only the material needed for the review. Neither access to the contract nor permission to review it should automatically authorize signing the renewal.

Build the approval step around the business action. A person can approve the final agreement after reviewing the legal result. Keep the requester, receiving agent, accessed records and approval in the AI audit trail. This is a hypothetical workflow, not a claim that either protocol supplies those controls by itself.

Which do you need: MCP, A2A or both?

Start with the workflow. If an assistant needs access to a database, document store or business tool, evaluate MCP. If independently operated agents need to exchange work, evaluate A2A. Use both when delegated agents also need tool access.

Do not choose solely by whether a job runs for a long time or returns a file. Those properties do not define the trust boundary. Focus on whether you are exposing a capability to an application or asking another agent to take responsibility for work.

• Choose MCP for standardized access to tools and context.
• Choose A2A for standardized communication between agents.
• Choose both when the workflow includes tool access and agent delegation.
• Before deployment, test denied access, approval requirements and the evidence left after an action.

Where Identra fits

Identra gives security teams an inventory of coding agents, desktop AI apps, MCP servers, skills and plugins on macOS and Windows. Teams can deny destructive shell commands by policy and review agent runs with the user, device, AI client and allowed or blocked outcome. Browser, endpoint and provider activity comes together in one identity timeline, where the person is known.

Frequently asked questions

Related terms

• AI Agent Identities vs Service Accounts: Access Needs an OwnerA service account gives software an identity for access.→
• Agentic AI Security vs LLM Security: Why Securing the Model Is Not Securing the AgentLLM security protects a model's inputs and outputs: prompt injection, jailbreaks, unsafe responses, and data leakage.→
• Prompt injection vs jailbreaking: Which boundary is at risk?Prompt injection redirects an AI application away from its intended task.→