AI Connectors Explained: MCP vs. APIs vs. A2A vs. Native Integrations

AI assistants are becoming much more useful because they are no longer limited to generating answers.

They can search company files, query databases, check calendars, create tickets, update records, run code and trigger workflows. Increasingly, they can also hand work to other AI agents.

But there are several different technologies behind those connections: APIs, the Model Context Protocol (MCP), native AI integrations and Agent2Agent (A2A). They overlap, but they are not interchangeable.

The simplest way to think about the emerging AI connector stack is this: APIs expose software capabilities, MCP gives AI applications a standard way to use tools and data, native integrations package those capabilities for a specific AI product, and A2A lets independent AI agents work with one another. Understanding those differences makes it much easier to choose the right architecture for an AI workflow — and it is the same vocabulary problem covered in what AI connectors actually are.

The AI connector stack at a glance

A single AI workflow may use several of these layers at once. An AI sales assistant could use MCP to expose a CRM tool; that MCP server might call the CRM’s REST API behind the scenes; a separate research agent could then communicate with the sales agent using A2A. These technologies are better understood as layers of the same system than as competing standards.

APIs are still the foundation

APIs existed long before today’s AI agents and remain a fundamental mechanism for software integration. An API defines how one software system can request information or actions from another. A CRM API might expose operations for retrieving a contact, updating an opportunity or creating a task.

AI models can already use those APIs through function calling. Google describes Gemini function calling (Google AI for Developers: Function calling with the Gemini API, opens in a new tab) as a way to connect models to external tools and APIs: the model determines which function is appropriate and supplies structured arguments, and the surrounding application executes the actual function.

Anthropic uses a similar model for Claude tool use (Anthropic: Tool use with Claude, opens in a new tab): developers define tools, Claude determines when one is appropriate, and the application or Anthropic infrastructure executes the requested operation.

That works well, but every AI application traditionally has to implement its own tool integrations. Imagine building an assistant that needs access to GitHub, Slack, Google Drive, Postgres and Jira. Without a common integration layer, you may need to define and maintain a separate interface for each service and for each AI application that uses it. MCP attacks that integration problem.

What is MCP?

The Model Context Protocol (Model Context Protocol: Model Context Protocol: introduction, opens in a new tab) is an open standard for connecting AI applications to external systems. An MCP server can expose tools an AI system can invoke, resources that provide data or context, and prompts that provide reusable workflows or instructions.

The official documentation compares MCP to USB-C for AI applications: instead of creating a new connection mechanism for every combination of AI application and external service, both sides can support a common protocol.

How MCP works

AI application→MCP client→MCP server→External system

Suppose an organisation builds an MCP server for its CRM. That server might expose tools such as find_contact, get_account, create_task and update_opportunity. The AI application can discover those tools, examine their descriptions and parameter schemas, decide which one fits the user’s request and invoke it. The MCP server can then call the CRM’s ordinary API.

That is why MCP does not replace APIs; it frequently sits on top of them. The specification explicitly describes tools that interact with external systems by doing things such as querying databases or calling APIs (Model Context Protocol: MCP specification: server tools, opens in a new tab). A useful mental model: the API exposes the capability, and MCP makes that capability easier for AI systems to discover and use in a standardised way.

Why MCP matters

The real advantage becomes clearer when multiple AI applications need access to the same tools. Without a standard, 5 AI applications × 10 systems can become dozens of bespoke integrations. With MCP, those external systems can expose standardised interfaces that multiple compatible AI clients understand.

That portability is increasingly practical because MCP is no longer limited to one AI platform. ChatGPT apps (OpenAI Help Center: Connectors in ChatGPT, opens in a new tab) can use MCP to call tools and retrieve information. Anthropic allows developers to connect the Claude API directly to remote MCP servers (Anthropic: MCP connector for the Claude API, opens in a new tab), including multiple servers in a single request. Google’s Gemini SDKs now include built-in MCP support (Google AI for Developers: Gemini API function calling: built-in MCP support, opens in a new tab), including automatic execution of MCP tool calls.

That creates the possibility of building a tool integration once and making it available across multiple AI environments rather than rebuilding the connector for each model provider. Portability is not guaranteed — individual hosts implement MCP features differently, as the three-platform connector comparison shows — but the common protocol substantially reduces the integration problem.

What are native AI connectors?

For most users, MCP itself may never be visible. They simply connect an app. Gemini lets users enable connected apps (Google Help: Connected apps in the Gemini app, opens in a new tab) that allow the assistant to interact with information and capabilities in other services.

ChatGPT similarly provides an app ecosystem for accessing external services and company information. OpenAI also allows developers to build custom apps using MCP, while the Apps SDK (OpenAI Help Center: Build with the Apps SDK, opens in a new tab) extends MCP with interfaces designed to run inside ChatGPT. A native connector or app is therefore primarily a product experience; MCP is a protocol. That distinction matters — and it is the same trade-off examined in connectors versus MCP servers.

Native integration

Best when you want simple setup, platform-managed authentication, an experience supported directly by the AI product, and minimal development work.

MCP integration

Best when you want custom tools, access to internal systems, greater control over available actions, potential compatibility across multiple AI clients, or an integration that isn’t limited to a single assistant’s native directory.

The two approaches can overlap. A native AI app may itself be powered by MCP. So asking whether you should use “a connector or MCP” is increasingly the wrong question: the connector may simply be the user-facing version of an MCP integration.

MCP vs. A2A: the most important distinction

MCP and A2A are frequently grouped together because both are open protocols associated with AI agents. But they solve different problems. MCP connects an AI system to capabilities. A2A connects one agent to another agent.

The A2A Protocol (A2A Project (Linux Foundation): A2A Protocol documentation, opens in a new tab) is designed to let agents built with different technologies, frameworks and vendors communicate and collaborate. Google originally introduced A2A in 2025, and the project is now hosted by the Linux Foundation; the protocol reached its first stable 1.0 specification in 2026, according to the Linux Foundation announcement (Linux Foundation: A2A protocol surpasses 150 organizations and reaches enterprise production use, opens in a new tab).

MCP example

A marketing agent needs yesterday’s campaign performance. It uses an MCP tool to query the company’s analytics system.

Agent→Analytics tool

A2A example

A marketing agent needs a detailed competitive analysis. Instead of doing the research itself, it discovers a specialised competitive-intelligence agent and delegates the assignment.

Marketing agent→Research agent

That research agent might then use MCP to access its own search, database and analytics tools, so the full architecture becomes:

Agent→A2A→Agent→MCP→Tools

The official A2A documentation summarises the relationship neatly: build an agent with your preferred framework, equip it with tools such as MCP, and use A2A to communicate with other agents.

Why not just use an API between agents?

Technically, you can. An agent could expose ordinary HTTP endpoints and another application could call them. A2A tries to standardise the additional concepts that become useful when the endpoint on the other side isn’t a deterministic software function but an autonomous agent.

One example is capability discovery. A2A agents can publish an Agent Card (A2A Project (Linux Foundation): A2A key concepts, including the Agent Card, opens in a new tab), a structured document describing the agent’s identity, endpoint, supported capabilities, authentication requirements and available skills. That allows another system to evaluate questions such as: what can this agent do, what input does it accept, does it support the capability I need, how do I authenticate, and where do I send the task?

The A2A specification (A2A Project (Linux Foundation): A2A specification, opens in a new tab) also defines concepts for messages, tasks, long-running work and task status rather than treating every interaction as a request followed immediately by a response. That matters more as agents begin delegating work that may take multiple steps or require asynchronous coordination.

From reading information to taking action

The most meaningful change in AI connectors isn’t the number of available integrations. It’s what those integrations allow AI systems to do. There is a fundamental difference between an assistant that can read information and an agent that can change an external system.

1. Generate — Draft a sales follow-up email.
2. Retrieve — Find the customer’s latest support tickets.
3. Act — Create a CRM task and schedule the follow-up.
4. Delegate — Ask a research agent to investigate the company before creating the account brief.

Modern connector infrastructure is steadily pushing AI systems from the first two levels toward the last two. One 2026 study examined 177,436 tools exposed through public MCP servers and classified them by whether they primarily enabled perception, reasoning or action; the researchers found that tools capable of changing external environments represented an increasingly important share of real-world usage. The MCP tool study (arXiv: A large-scale study of tools exposed through public MCP servers, opens in a new tab) documents the methodology and results.

More capability also creates more risk

A connector that can search a document library has a fundamentally different risk profile from one that can delete files, send emails or modify production infrastructure. The important question is no longer simply whether the AI can connect to a system; it is what authority that connection grants. Before enabling an action-capable connector, evaluate at least five things — and the practical review process is set out in how to evaluate connector permissions.

Authentication

How does the system establish who is connecting? MCP includes an authorization framework (Model Context Protocol: MCP specification: authorization, opens in a new tab) for HTTP-based connections.

Authorisation

What is the connected user or agent actually allowed to do? Follow least-privilege principles: expose only the tools and permissions the workflow actually requires.

Human approval

Which actions should require confirmation? Reading a public knowledge base might happen automatically. Sending an external email, transferring money, deleting data or changing production infrastructure deserves a much higher bar — a distinction OpenAI draws for action-capable apps in developer mode (OpenAI Help Center: Developer mode and MCP apps in ChatGPT, opens in a new tab).

Visibility

Can users tell which tool the agent intends to use and what it will do? Research into MCP implementations has identified risks including misleading tool metadata, tool poisoning and discrepancies between documented behaviour and actual execution; see this behaviour study (arXiv: Documented versus actual behaviour in MCP tool implementations, opens in a new tab) and security analysis (arXiv: Security analysis of the Model Context Protocol ecosystem, opens in a new tab).

Auditability

Can you determine what happened afterward? Production AI workflows should make it possible to trace who requested an action, which agent selected it, which tool ran, what arguments were used and what changed. The more autonomy an agent receives, the more important that trail becomes.

An MCP server is software, not a plugin

The rapid growth of MCP has made it easy to find community-built servers for almost any imaginable service. That convenience can create a dangerous assumption: if an MCP server exists, it must be safe to connect. It isn’t. An MCP server is software with access to whatever credentials, files, services or systems you make available to it.

Anthropic’s remote server guidance (Anthropic: Remote MCP servers, opens in a new tab) advises users to connect only to servers they trust and to review a third-party server’s security practices before connecting. Researchers have also found vulnerabilities across real-world implementations in an open-source MCP security study (arXiv: Systematic security analysis of open-source MCP servers, opens in a new tab).

For important systems, evaluate an MCP server much as you would any other software dependency:

• Who built it?
• Is it maintained?
• Is there an official server from the service provider?
• What credentials does it require?
• What tools does it expose?
• Can those tools write or delete data?
• Does the server send information anywhere else?
• Can access be revoked?
• Are actions logged?
• Can the integration be restricted to specific users or environments?

Connector discovery is only the beginning. Connector trust is becoming equally important, which is why every record in the connection directory is tied to official documentation and a verification date.

Which integration approach should you use?

Use a native AI integration when…

You want a supported connection between an AI assistant and a common service and don’t need much customisation — for example, letting Gemini retrieve information from Gmail or Google Drive. This is generally the lowest-friction option.

Use MCP when…

You want an AI system to interact with external data or tools through a reusable, standardised interface — for example, giving several AI assistants access to your company’s internal product database. MCP is especially compelling when the same tools may eventually be used by multiple models or AI clients.

Use direct APIs or function calling when…

You control the application and need a narrow, deterministic integration — for example, your AI application invoking exactly three backend functions your engineering team owns. Adding an MCP layer isn’t automatically useful.

Use A2A when…

The thing on the other side of the connection is itself an independent agent capable of receiving and performing delegated work — for example, a support agent delegating a refund investigation to a specialised payments agent.

Combine them when…

1. A customer asks a service agent to investigate an issue.
2. The service agent uses MCP to retrieve CRM and support data.
3. It uses A2A to delegate a billing investigation to a payments agent.
4. The payments agent uses its own MCP tools.
5. Those MCP servers call underlying APIs to retrieve and update data.
6. A native AI interface presents the process and asks the user to approve a sensitive action.

A practical decision tree

1. 1. Does the AI need external information?

   • No → you may not need a connector at all.
   • Yes → continue.

2. 2. Is there already a trusted native integration that does exactly what you need?

   • Yes → start there.
   • No → continue.

3. 3. Are you exposing a tool or data source to an AI application?

   • Yes → consider MCP or direct function calling.

4. 4. Is this a small integration tightly coupled to one application?

   • Yes → a direct API or function call may be simpler.

5. 5. Do you want the capability reusable across compatible AI clients?

   • Yes → MCP becomes more attractive.

6. 6. Does another autonomous agent need to perform the work?

   • Yes → consider A2A.

7. 7. Can the integration change external systems?

   • Yes → add explicit authorisation, least-privilege access, logging and appropriate human approval before treating the workflow as production-ready.

The bigger picture: connectors turn AI into an interface for software

The first wave of generative AI was primarily about what a model could produce. The connector layer changes the question. Instead of asking what the model knows, we can increasingly ask what the system can access and what it can do. That makes connector architecture a major part of AI capability.

• APIs provide access to software.
• MCP makes tools and data easier for AI systems to use through a common protocol.
• Native integrations package those capabilities into products ordinary users can connect.
• A2A creates a common language for agents that need to discover, communicate with and delegate work to other agents.

The likely future isn’t one standard defeating the others. It’s an AI stack in which they work together: APIs underneath, MCP between agents and tools, A2A between agents, and native apps and connectors on top.

For teams evaluating AI platforms, the connector question is becoming much bigger than “does this app integrate with Salesforce?”. The more useful questions are what the AI can access, what it can change, how portable the integration is, who controls its permissions and whether it can safely work with the rest of your agent ecosystem. Those questions will increasingly determine what an AI system can actually accomplish — and they are the ones we test against vendor documentation for every assistant platform we cover, using the criteria set out in our verification methodology.