MCP Is Just a Connector Standard. Here's Why It Matters.

Model Context Protocol (MCP) has moved fast from an Anthropic internal spec to something most AI tooling vendors are adopting. Microsoft is no exception. If you’re building anything with Copilot Studio, Azure AI Foundry, or custom agents that touch Power Platform data, MCP is the plumbing you’ll be dealing with.

This isn’t a tutorial on the MCP spec itself. It’s about how MCP fits into Power Platform specifically — what’s shipped, what’s in preview, and what the realistic patterns look like.

What MCP Actually Is

MCP is a protocol that lets an AI model call external tools and read external context through a standardized interface. Instead of every AI product inventing its own plugin or connector format, an MCP server exposes a set of tools (callable functions) and resources (readable data) that any MCP-capable client can use.

The architecture is simple:

The MCP server sits between the AI and your actual data. The AI asks “what tools do you have?” The server responds with a list. The AI decides which tool to call. The server executes it and returns a result. The AI uses that result in its response.

This is roughly how Power Automate flows and Azure Functions have always worked when called from Copilot Studio — the difference is that MCP standardizes the discovery and invocation contract so you’re not writing custom connector glue every time.

What Microsoft Has Shipped

Microsoft has been rolling out MCP server support across several surfaces. The picture as of mid-2026:

Copilot Studio can act as both an MCP client and host MCP servers. When you publish an agent in Copilot Studio, you can expose its actions as an MCP server — meaning other agents or tools can call into it using the standard protocol. Conversely, a Copilot Studio agent can call external MCP servers as tools.

Power Automate flows can be surfaced as MCP tools. A flow with a manual trigger (HTTP request trigger, specifically) becomes callable by any MCP client that knows about it. This is the lowest-friction path to exposing existing automation as AI-callable tools — you probably already have flows that do the right thing, and wrapping them as MCP tools is mostly configuration rather than new code.

Dataverse has MCP exposure in preview through the Power Platform Connector for MCP. It exposes Dataverse operations — query, create, update, delete — as discoverable tools. An AI agent can ask “find all open cases for this customer” and the MCP server translates that into an OData query against your environment without you writing the connector.

Azure AI Foundry agents can call MCP servers natively, which is the bridge between custom Azure-hosted AI and Power Platform data. If you’re building an agent in Foundry and need it to read or write Dataverse, an MCP server is cleaner than building a custom REST API wrapper.

The Pattern That Works Today

The most practical MCP pattern in Power Platform right now:

1. Expose Power Automate flows as MCP tools. Use an HTTP-triggered flow for each discrete action your agent needs to perform. Keep each flow focused — one tool does one thing. The flow handles Dataverse operations, approvals, notifications, whatever the business logic requires.

2. Use Copilot Studio as the MCP client. Configure your agent to call those flows as tools. Copilot Studio handles the conversation layer and decides when to invoke which tool based on user intent.

3. Let Dataverse be the data layer, not the tool. Rather than exposing raw Dataverse CRUD as MCP tools (which gives an AI agent too much latitude to write anything anywhere), expose specific business operations as flows. “Create a new support case with these details” is a better MCP tool than “insert a row into the incident table.”

The flows act as a controlled interface. The agent can only do what you’ve explicitly exposed as a tool. That’s intentional.

What’s Still Being Figured Out

Authentication is the friction point. MCP doesn’t dictate how auth works — that’s left to the server implementation. Power Platform’s MCP surfaces use Azure AD (Entra ID) with OAuth, which is fine for agents running in the same tenant. For cross-tenant scenarios, or agents hosted outside Azure calling into your Dataverse, the auth setup gets complicated quickly. Expect to spend time here.

Tool granularity is a design problem. Too coarse and the agent can’t do useful things (“do everything related to this customer” is not a tool). Too fine and the agent makes too many round trips to accomplish simple tasks. The right level is usually one tool per business operation, not one tool per database table.

Stateless tools need stateful context. MCP tools are called independently. If your business process requires state across multiple tool calls (“first check if the customer exists, then create a case, then assign it”), you’re either putting that orchestration in a single flow or designing the agent prompt to handle multi-step sequences. Both work; neither is obvious until you’ve hit the problem.

Dataverse MCP exposure is still maturing. The native Dataverse MCP connector is useful for read-heavy scenarios but exposes a lot of surface area. For anything involving writes or complex business logic, wrapping in Power Automate flows first is still the right call.

Testing MCP Servers

The MCP Inspector is a standalone tool that connects to any MCP server and lets you call its tools directly, outside of any AI client. Use it during development — it removes the AI layer so you can verify the tool itself works before debugging why the AI isn’t calling it correctly.

npx @modelcontextprotocol/inspector

For Power Platform flows exposed as MCP tools, the Inspector lets you send test payloads and see responses before you’ve wired up Copilot Studio. Most bugs at this layer are auth configuration or JSON schema mismatches in the tool definition, and the Inspector surfaces both faster than going through the full agent.

Honest Assessment

MCP in Power Platform is genuinely useful today for specific scenarios: surfacing existing flows to AI agents, connecting Copilot Studio to external systems, and bridging Power Platform to Azure AI workloads. It’s not a magic layer that makes AI agents production-ready — it’s a protocol that standardizes one specific part of the integration problem.

The patterns that work are the ones where you’re exposing deliberate, bounded operations to an AI that orchestrates them. The patterns that don’t work yet are anything requiring fine-grained auth across tenants, complex state management across tool calls, or raw Dataverse access without a business logic layer in front of it.

Check the Power Platform release notes. This space is moving weekly, and something that was preview in Q1 2026 may be GA by the time you’re reading this.