How to Choose Between A2A, MCP, and Agent Name Service
These capabilities solve different problems; selection starts with task handoff, tool access, identity discovery, and governance.
Key takeaways
A2A, MCP, and Agent Name Service are not three interchangeable products. A2A primarily supports task, status, and result exchange between agents. MCP commonly connects models or agents with tools, data, and context. The proposed Agent Name Service focuses on neutral naming, discovery, and authenticity infrastructure. Selection should begin with the workflow rather than the protocol label. Map the data path, runtime identities, permission scopes, protocol versions, logs, approval gates, and revocation route. A small local workflow may need only a direct tool connector. A cross-vendor multi-agent workflow may need A2A plus identity discovery and an authorization layer. The correct architecture is the smallest combination that makes every handoff observable, constrained, and recoverable.
# How to Choose Between A2A, MCP, and Agent Name Service
Published: July 22, 2026
Table of contents
- Direct answer
- Fact sources
- Definition, scenarios, steps, and risks
- Why it matters
- Impact for ordinary AI users
- Related tools/tutorials
- FAQ
- Source links
Direct answer
Evaluate A2A for task handoff between agents, MCP for connecting agents to tools and data, and Agent Name Service for cross-organization naming, discovery, and authenticity. Production systems also need identity, authorization, gateways, logs, and approval.
Fact sources
On July 17, 2026, the World Internet Conference Asia-Pacific Summit released an initiative calling for mutual trust, connectivity, and interoperability among AI agents. Its nine proposals cover ecosystem development, security governance, open-source collaboration, standard interfaces, privacy and data protection, and closing digital divides. A chair's statement published the same day called for international consensus, coordinated standards, and responsible agent development. Earlier, the ITU announced a Focus Group on identity and access management for agentic AI on July 9. The Linux Foundation announced its intent to launch Agent Name Service on June 23, 2026 for neutral naming, discovery, and authenticity checks, while its A2A project supports agent-to-agent communication. These are cooperation and standards-building efforts, not a single mandatory global standard already deployed everywhere.
Definition, scenarios, steps, and risks
Use a six-part matrix: target object, task type, data location, runtime identity, execution permissions, and recovery. A2A fits agent collaboration, MCP fits tool and context access, and ANS fits large-scale discovery and identity resolution.
- List agents, models, tools, data sources, and external accounts before selecting a protocol.
- Classify every handoff as task communication, tool access, identity discovery, or authorization.
- Define read, write, external-send, paid-action, and delete permissions for every runtime identity.
- Compare maturity, implementations, version compatibility, logging, and vendor lock-in.
- Test the smallest component combination with a low-risk example, including failure, timeout, revocation, and rollback.
- Add a gateway, directory, or protocol layer only when a simpler design cannot meet verifiable requirements.
Selection risks include stacking components to chase standards, equating open protocols with security, ignoring identity and authorization, leaving versions unpinned, and chaining vendors without complete logs.
Why it matters
As agent ecosystems expand, users will encounter protocols, directories, gateways, identity services, and tool connectors together. Problem-first selection reduces duplication, lock-in, and unclear responsibility.
Impact for ordinary AI users
Individuals and small teams do not need a full stack immediately. Confirm whether multiple agents are necessary, begin with read-only tool access or one task handoff, and add discovery or identity layers only when required.
Related tools/tutorials
Compare tools in ENHE AI software, review authorization in account services, build trials through skill tutorials, and track protocol changes in frontier news.
Related ENHE AI links: 工具选型指南 examples, AI software and local deployment tools, AI account services and permission management, AI skill tutorials and validation methods, ENHE AI homepage.
FAQ
Are A2A and MCP the same protocol?
No. A2A primarily handles task communication and status exchange between agents, while MCP commonly connects models or agents to tools, data, and context. They can be combined.
Is the global agent cooperation initiative already a mandatory standard?
No. It is a cooperation initiative released on July 17, 2026, while related standards, identity infrastructure, and protocol governance continue to develop.
Why should ordinary users care about agent interoperability?
More AI software will connect email, calendars, storage, code, accounts, and other agents, making identity, permissions, logs, revocation, and rollback direct user concerns.
Source links
- 新华社：推动全球智能体互信互联互通合作倡议
- 中华人民共和国外交部：世界互联网大会亚太峰会主席声明
- ITU: Focus Group on Identity and Access Management for Agentic AI
- 中央网信办：构筑智能体创新发展的安全底座
- Linux Foundation: Agent Name Service trusted identity infrastructure
- Linux Foundation: Agent2Agent Protocol Project
What this means for everyday users
ENHE selection guidance should tie every recommendation to a target scenario and verification check, such as one read-only call, one revocation, and one log trace in a test account.
Related tutorials
Related reading
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Summary
Good selection is not about finding one winning protocol. It uses the fewest components needed for communication, tools, identity, and governance while keeping every handoff observable, constrained, and recoverable.
