The Two Dominant Protocols
The AI agent ecosystem is converging around two major communication protocols: Anthropic's Model Context Protocol (MCP) and Google's Agent-to-Agent (A2A) protocol. Both solve the interoperability problem, but they approach it from different angles and serve different use cases.
AgentDNS indexes agents from both ecosystems. Understanding the difference helps you choose the right protocol for your agent and find the right agents to integrate with.
MCP: Tools for AI Models
MCP connects AI models to external tools and data sources. Think of it as a universal adapter between an LLM and the systems it needs to interact with. A database, a file system, a browser, an API. MCP provides a standardized way for the model to discover what tools are available and how to use them.
How MCP Works
An MCP server exposes capabilities (called "tools") that an AI model can invoke. The model's host application connects to MCP servers and presents their tools to the model. When the model decides to use a tool, the host application forwards the call to the MCP server and returns the result.
Key characteristics:
- Tool-centric. MCP servers are tools, not autonomous agents. They wait for instructions.
- Model-driven. The AI model decides when and how to use MCP tools.
- Local-first. MCP servers often run locally alongside the model (in VS Code, Claude Desktop, etc.).
- Synchronous. Request-response pattern. The model calls a tool and waits for the result.
MCP Ecosystem
Over 9,000 MCP servers are indexed on AgentDNS. The ecosystem exploded after Anthropic open-sourced the protocol and major editors (VS Code, JetBrains) integrated native MCP support. The official MCP Registry, backed by Anthropic, GitHub, and Microsoft, provides a curated list of verified servers.
A2A: Agent-to-Agent Communication
A2A enables autonomous agents to communicate with each other. Unlike MCP's tool paradigm, A2A treats each participant as a peer. Agents can negotiate, delegate tasks, share state, and compose workflows without human orchestration.
How A2A Works
Each A2A agent publishes an Agent Card at /.well-known/agent.json describing its capabilities, skills, and communication preferences. A client agent discovers a remote agent via its Agent Card, then sends tasks using a standardized protocol. The remote agent can stream responses, send progress updates, or request clarification.
Key characteristics:
- Agent-centric. Both participants are autonomous agents, not passive tools.
- Peer-to-peer. No single model is in control. Agents negotiate and collaborate.
- Remote-first. A2A agents are web services that communicate over HTTPS.
- Asynchronous. Supports streaming, push notifications, and long-running tasks.
A2A Ecosystem
A2A launched in 2025 with 50+ partners including Salesforce, SAP, and Atlassian. Google Cloud's Agent Finder marketplace lets enterprises discover and procure A2A-compatible agents. The protocol was donated to the Linux Foundation, signaling long-term neutrality.
Side-by-Side Comparison
| Aspect | MCP | A2A |
|---|---|---|
| Primary use case | Connect models to tools | Agent-to-agent workflows |
| Communication | Synchronous (request/response) | Async (streaming, notifications) |
| Discovery | MCP Registry, editor integrations | Agent Cards at /.well-known/agent.json |
| Autonomy | Model-driven (tools are passive) | Peer-to-peer (agents are autonomous) |
| Deployment | Often local (stdio, SSE) | Remote web services (HTTPS) |
| Governance | Agentic AI Foundation (Anthropic) | Linux Foundation (Google) |
| Ecosystem size | 9,000+ servers | 50+ enterprise partners |
When to Use Which
Use MCP when you're building a tool that AI models should be able to use. Database connectors, file system access, API wrappers, browser automation. If your agent's job is to execute commands on behalf of a model, MCP is the right fit.
Use A2A when you're building an autonomous agent that needs to collaborate with other agents. Multi-step workflows, task delegation, cross-organization agent networks. If your agent makes decisions and negotiates with other agents, A2A is the right fit.
Use both when your agent needs maximum discoverability. Many agents expose both an MCP interface (for tool-mode usage) and an A2A endpoint (for agent-mode collaboration). AgentDNS supports registering agents with both protocols.
Finding Agents by Protocol
AgentDNS lets you filter agents by protocol. Search for MCP-only agents, A2A-only agents, or agents that support both:
curl "https://agent-dns.tech/api/v1/resolve?capability=code-execution&protocol=mcp"
curl "https://agent-dns.tech/api/v1/resolve?capability=code-execution&protocol=a2a"Or use the explore page filters to browse by protocol visually.
The protocol landscape is still evolving. By indexing both MCP and A2A (plus REST, GraphQL, and WebSocket agents), AgentDNS ensures you can discover agents regardless of which protocol wins or whether both coexist long-term.