Some links on this page are affiliate links: if you buy through them we may earn a commission, at no extra cost to you.
The two protocols are Anthropic’s Model Context Protocol (MCP) and Google-originated Agent2Agent (A2A). MCP connects an AI application or agent to tools, data, and services; A2A lets independent agents discover one another, communicate, and delegate work. They address different connections and can be used together: one agent can delegate a task over A2A while the specialist agent uses MCP to reach its own tools.
What “agentic internet” means here
In this context, the agentic internet is a way to think about AI agents and the systems around them communicating through shared conventions rather than a collection of one-off integrations. The phrase does not name a single network, product, or protocol. MCP and A2A tackle two different integration problems within that broader idea.
![Computer Networking Bible: [3 in 1] The Complete Crash Course to Effectively Design, Implement and Manage Networks. Including Sections on Security, Performance and Scalability](https://m.media-amazon.com/images/I/41H4YJnxKgL._SL160_.jpg)
An AI assistant may need access to a search tool, a company database, or a development environment. It may also need to ask a separate, independently operated agent to complete a task. Those are not the same relationship. MCP standardizes the first kind of connection; A2A standardizes the second.
What is MCP?
Anthropic announced the Model Context Protocol on November 25, 2024, describing it as an open standard for connecting AI assistants to systems where data lives, including content repositories, business tools, and development environments. Its aim is to reduce the need for a custom integration every time an AI application connects to a different data source or service.
#1 Best Overall
How the MCP relationship works
A useful mental model has three parts: an AI host or agent, an MCP client that speaks the protocol, and an MCP server that exposes capabilities. Those capabilities can include tools or resources. The client can discover what is available and ask to use it through a consistent interface, while the underlying service remains separate from the model.
For example, an assistant that needs information from a company system could use an MCP server exposing the relevant data or actions. The assistant does not need that service to be part of the model itself. MCP describes the connection boundary between the AI application and the capability it wants to use.
What MCP is useful for
- Connecting an AI application to tools or data sources through a shared protocol rather than building a distinct integration for each connection.
- Making capabilities available in a form an MCP client can discover and invoke.
- Keeping a tool or data service distinct from the AI model that requests it.
MCP is not, by itself, a general-purpose protocol for delegating a whole task to another independent agent. That is the boundary A2A is designed to address.
What’s actually slowing this PC down?
Pick the symptom - the matching free tool is one click away.
What is A2A?
The Agent2Agent Protocol is an open standard for communication and interoperability between independent, potentially opaque AI agent systems. It is intended to let one agent discover another agent’s capabilities, negotiate how to interact, and manage collaborative tasks without requiring the other agent to disclose its internal state, memory, or tools.
Google originated A2A. In June 2025, the Linux Foundation announced the project under its stewardship. Its focus is the connection between agents, including agents built on different frameworks or by different vendors.
How an A2A interaction differs
With a direct tool call, the caller selects a capability and generally manages the call itself. With agent-to-agent collaboration, the caller can request an outcome from another agent that retains its own workflow. The receiving agent may be able to carry out work without exposing every internal step to the delegating agent.
For example, a general-purpose assistant could ask a specialist agent to analyze a particular task and return a result. The caller needs to communicate the request and receive the response; it need not know all the specialist’s internal tools or how it organizes its work.
What A2A is useful for
- Discovering what another agent can do before requesting work.
- Negotiating interaction formats such as text, files, or structured data.
- Delegating collaborative tasks across independent agents without assuming they share an implementation.
A2A does not replace the tools an agent uses internally. It standardizes a different boundary: communication with another agent.
MCP vs. A2A: the practical differences
| Question | MCP | A2A |
|---|---|---|
| What connects? | An AI application or agent to a tool, data source, or service. | One independent agent to another independent agent. |
| What is the main activity? | Discovering and invoking a capability. | Discovering, communicating, delegating, and collaborating. |
| Who manages the work? | The caller generally selects and manages tool calls. | The delegated-to agent retains its own workflow while responding to a request for an outcome. |
| What boundary does it standardize? | External systems and data integrations. | Cross-vendor or cross-framework agent interaction. |
| Plain-language metaphor | A common connector from an agent to tools and data. | A common language for agent collaboration. |
You may also see MCP described as “vertical” and A2A as “horizontal”: MCP connects an agent down to capabilities, while A2A connects it across to another agent. That is a helpful explanatory shorthand, not a formal distinction the protocols require.
Can MCP and A2A work together?
Yes. They are complementary, not competing choices. An orchestrating agent can use A2A to find and delegate to a specialist. That specialist can use MCP internally to access a search service, database, code tool, CRM, or another capability. The orchestrator receives the specialist’s result through A2A without needing to coordinate every MCP server or internal step itself.
A simple workflow
- Identify the task. The orchestrator decides it needs work that another agent is suited to perform.
- Find a suitable agent. It uses A2A’s agent capability-discovery and interaction model to identify a peer and establish how to communicate.
- Delegate the outcome. The orchestrator sends the task to the independent agent over A2A.
- Use internal capabilities. The specialist agent may use MCP to discover and invoke tools or data sources needed for its work.
- Return the result. The specialist communicates its result back to the orchestrator through A2A.
This division lets each protocol do its own job. A2A handles the relationship between agents; MCP handles an agent’s access to external capabilities. An architecture can use either protocol without the other, or combine them where both kinds of connection are needed.
Free tools Windows power users keep installed
One-click scans. No signup required.
Which protocol should a developer use?
Choose MCP when the integration is to a tool or data source
If an AI application needs a consistent way to discover and invoke capabilities exposed by an external service, MCP is the relevant protocol boundary. Think “the agent needs access to this capability.”
Choose A2A when the integration is to another agent
If the other system is an independent agent and the goal is capability discovery, interaction, or task delegation, A2A is the relevant boundary. Think “the agent needs another agent to produce an outcome.”
Use both when an agent delegates and also needs tools
If an orchestrator delegates to a specialist and the specialist needs access to external tools or data, the two protocols can be layered. Avoid using the existence of one protocol as a reason to force it into the other’s role.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Design and implementation questions to settle
A protocol standardizes a communication boundary; it does not decide every application-level policy for you. Before connecting tools or delegating work, decide what your application is willing to expose, which requests it will accept, what information may be returned, and how failures should be surfaced to users.
The Tool Desk
Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →- Define responsibility. Be clear about which agent owns a task and which system is responsible for its result.
- Expose only intended capabilities. Decide which tools or resources an MCP server makes available and what an A2A-connected peer may be asked to do.
- Set interaction expectations. Agree on the expected inputs, output formats, and what counts as a useful result for your application.
- Handle incomplete work. Decide what the caller should do if a tool call fails, a peer cannot complete a task, or a returned result does not meet the request.
- Keep the boundary visible. Log or otherwise track which tool or agent handled a request, consistent with your application’s privacy and security requirements.
These are system-design decisions, not claims that either protocol automatically guarantees a particular security outcome or application behavior. Check the current official specifications before implementing: protocol versions, governance, and supported behavior can change.
Best Value
- Used Book in Good Condition
A practical MCP example: ScreenshotNeo
ScreenshotNeo is a website screenshot API and MCP server for developers, made by Yorker Media. It illustrates the distinction between a tool-facing MCP integration and an agent-to-agent protocol: its MCP server exposes screenshot-related tools for AI agents, while A2A addresses communication between independent agents. ScreenshotNeo is an example of an MCP-connected capability, not an A2A substitute. Learn more at ScreenshotNeo.
For a direct API request, the following cURL example captures a page as WebP; see the ScreenshotNeo documentation for the API details:
curl -G "https://api.screenshotneo.com/v1/shot" -d access_key=YOUR_API_KEY --data-urlencode url=https://example.com -o shot.webpScreenshotNeo’s MCP server provides take_screenshot, get_page_info, and capture_pdf tools. Its clean-shot options accept cookie or consent banners and remove more than 60 known consent platforms, newsletter popups, and chat widgets before capture; each step can be turned off. Responses identify page verdict and billing status in headers, and bot checks, blank pages, timeouts, failed loads, and cache hits are not billed. The service offers 1,000 shots per month free without a card; paid plans start at $5 for 3,000 shots. Every feature is available on every plan.
Recommended Free Tools
Try ScreenshotNeo free: sign up for 1,000 screenshots a month with no card.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
