AIPC and MCP: Complementary Protocols

One of the most common questions we receive is: "How does AIPC relate to Anthropic's Model Context Protocol?" It is a good question, and the answer reveals something important about the current AI infrastructure landscape: we are building complementary layers, not competing ones.

What MCP Does

The Model Context Protocol (MCP) standardizes how AI models connect to external tools and data sources. Before MCP, every AI platform had its own proprietary way of defining tool integrations. If you built a financial data tool for ChatGPT, you had to rebuild it for Claude, and again for Gemini. MCP provides a universal interface: define your tool once, and any MCP-compatible AI client can use it.

MCP solves the connectivity problem. It answers the question: "How does an AI agent discover, authenticate with, and call external tools?"

This is an important problem, and MCP solves it well. But connectivity is only half the story.

What MCP Does Not Do

MCP does not specify anything about how the data returned by a tool should be presented to the end user. When an MCP tool returns a JSON response containing mutual fund performance data, MCP's job is done. The data has been successfully retrieved and delivered to the AI model.

What happens next — how the AI formats that data, what disclosures it includes, whether it adds the required legal disclaimers — is entirely outside MCP's scope. And it should be. MCP is a transport and interface layer. Presentation requirements are a different concern entirely.

This is the gap AIPC fills.

What AIPC Does

AIPC standardizes how tool responses must be presented. It does not care how the data got to the AI model — whether it came through MCP, through direct API calls, through function calling, or through any other mechanism. AIPC only cares about what happens after the data arrives.

An AIPC contract wraps a data payload with enforceable presentation rules: required disclosures, formatting specifications, freshness constraints, tone controls, and fail-safe behavior. When an AI runtime processes an AIPC-wrapped response, it knows exactly how to present the data compliantly.

AIPC solves the presentation problem. It answers the question: "Now that the AI has this data, how must it present it to the user?"

How They Work Together

The relationship between MCP and AIPC is not competitive — it is compositional. An MCP tool can return an AIPC-wrapped response. The MCP layer handles the tool discovery, authentication, and invocation. The AIPC layer handles the presentation compliance of the response.

Here is a concrete example. Imagine an MCP server that provides financial data:

// MCP Tool Definition
{
  "name": "get_fund_performance",
  "description": "Get mutual fund performance data",
  "parameters": {
    "ticker": { "type": "string" }
  }
}

// MCP Tool Response (with AIPC envelope)
{
  "aipc_version": "0.1.0",
  "provider": {
    "id": "morningstar",
    "name": "Morningstar, Inc."
  },
  "contract": {
    "disclosures": [{
      "text": "Past performance is not indicative of future results.",
      "verbatim": true,
      "priority": "required",
      "placement": "adjacent",
      "applies_to": "returns.*"
    }],
    "display_rules": [{
      "field": "returns.1yr",
      "format": {
        "type": "percentage",
        "precision": 2,
        "sign": "always"
      }
    }],
    "attribution": {
      "required": true,
      "text": "Data provided by Morningstar, Inc."
    },
    "fail_behavior": "suppress"
  },
  "payload": {
    "ticker": "VFIAX",
    "name": "Vanguard 500 Index Fund Admiral Shares",
    "returns": { "1yr": 0.1247, "5yr": 0.1103 }
  }
}

The MCP client calls the tool and receives this response. An AIPC-aware runtime then processes the contract, formats the data according to the display rules, injects the required disclosures, and generates a compliant output. If the runtime cannot satisfy the contract, the fail-safe behavior kicks in and the data is suppressed.

From the MCP perspective, the tool returned a JSON response. From the AIPC perspective, the response carried its own presentation rules. Neither protocol needs to know the details of the other. They compose naturally.

The Layer Cake

Think of the AI data stack as a series of layers:

1. Transport — HTTP, WebSocket, gRPC. How bytes move between systems.
2. Interface — MCP, function calling, tool-use APIs. How AI models discover and invoke tools.
3. Presentation — AIPC. How tool responses must be shown to users.
4. Rendering — Chat UIs, voice interfaces, dashboards. Where the user actually sees the output.

Each layer has its own concerns and its own standards. MCP operates at layer 2. AIPC operates at layer 3. They are adjacent, complementary, and independent. You can use MCP without AIPC (you just lose presentation compliance). You can use AIPC without MCP (the contract can wrap any API response, regardless of transport). But using both together gives you the full stack: standardized connectivity and standardized presentation.

Why This Matters

The AI ecosystem is maturing rapidly, and maturation means specialization. Just as the web stack separated transport (TCP/IP), application protocol (HTTP), content structure (HTML), and presentation (CSS), the AI stack is separating into distinct, composable layers.

MCP's adoption is accelerating because it solved a real problem: tool integration fragmentation. AIPC exists because there is an equally real problem one layer up: presentation compliance. As AI agents move from experimental toys to production systems handling regulated data, the need for standardized presentation contracts will become as obvious as the need for standardized tool interfaces.

The good news is that these protocols do not compete for the same territory. An MCP server developer can add AIPC contracts to their tool responses today, without changing anything about their MCP implementation. An AIPC runtime can process contracts from any source, whether or not MCP is involved. The protocols enhance each other without creating dependencies.

We are in active discussions with the MCP community about documenting best practices for AIPC-in-MCP patterns. If you are building MCP tools that return data with presentation requirements — particularly in regulated industries — we would love to hear from you. Join the community and help us define how these layers work together.

The AI stack needs both connectivity and compliance. MCP handles one. AIPC handles the other. Together, they complete the picture.