Model Context Protocol (MCP) had a banner year in 2025. Since MCP was first released in November 2024, the protocol has exploded with thousands of public MCP servers and millions of monthly SDK downloads. Everyone from Microsoft to Google has adopted MCP in their quest for agentic AI.

However, right when MCP was celebrating its anniversary, a new tool-calling protocol hit the market. Known as Universal Tool Calling Protocol (UTCP), it’s being billed as a lightweight, direct alternative to MCP. Below, we’ll compare MCP vs. UTCP to help you decide which protocol will be the best standard for your agentic AI ecosystem.

Introducing the Model Context Protocol (MCP)

MCP is an open standard that defines how AI applications can connect to external tools using a structured intermediary layer. MCP introduces a client-server model where an MCP server exposes tools, data sources, and actions in a standardized format. AI clients connect to this MCP server to discover available capabilities and invoke them using a uniform protocol. The goal of MCP is to normalize tool access so that models don’t need to understand the idiosyncrasies of every underlying API or service they interact with.

Advantages and Disadvantages of MCP

MCP is built around the idea of abstraction. Tools are registered with an MCP server, which exposes them using a consistent schema regardless of how they are implemented internally. From the AI client‘s perspective, all tools look the same as they are all built using the same format. This abstraction reduces the burden on the model or client application, since it only needs to speak one protocol. Its early support by major AI vendors is another major benefit of MCP, as it’s allowed the protocol to develop a healthy ecosystem of compatible tools and servers in a very short amount of time.

MCP is not without its drawbacks, though. Every tool must be wrapped using an MCP-compatible server, which introduces additional infrastructure to deploy, maintain, and secure. Each tool call passes through this intermediary layer, adding latency and additional failure modes.

Because MCP servers often handle credentials and execution logic, they also expand the security surface area. For teams operating large fleets of tools or latency-sensitive systems, these costs can become significant, even if the standardization benefits are appealing.

Example: MCP Protocol JSON

The following simplified example illustrates how a tool invocation might be expressed using MCP’s JSON-RPC style messaging.

{
  "jsonrpc": "2.0",
  "id": "1234",
  "method": "call_tool",
  "params": {
    "tool": "weather_service",
    "args": {
      "location": "Portland, OR"
    }
  }
}

In this tooling call model, the AI client asks the MCP server to execute a named tool with a structured set of arguments, and the server is responsible for translating that request into the underlying tool call and returning the result.

Introducing the Universal Tool Calling Protocol (UTCP)

UTCP approaches the area of agentic tool-calling from a more minimal and direct perspective. Instead of placing a protocol server between the AI and the tool, UTCP defines a descriptive JSON “manual” that explains how a tool should be called using its native interface. Once an AI agent reads this manual, it communicates directly with the tool over HTTP, WebSocket, gRPC, or another supported protocol. UTCP deliberately avoids acting as an execution proxy, aiming instead to standardize description and invocation guidance instead of execution itself.

Advantages and Disadvantages of UTCP

UTCP removes the intermediary layer entirely. Instead of proxying tool calls, UTCP standardizes how tools describe themselves. A UTCP document tells an AI agent what a tool does, which protocol it uses, how parameters should be supplied, and what form the response will take. After reading this description, the AI communicates directly with the tool using its native interface. This design with UTCP significantly reduces runtime overhead, since there is no additional server in the call path.

Efficiency and flexibility are the main benefits of UTCP. Because UTCP is protocol-agnostic by design, it can describe and invoke tools exposed over a wide range of transports, including RESTful HTTP APIs, gRPC services, WebSockets, CLI commands, database drivers, message queues, and even local or embedded interfaces. This means UTCP can also describe tools that themselves implement MCP, allowing MCP to function as one protocol among many instead of as a required execution layer. In this sense, UTCP does not compete with MCP it incorporates it.

Direct calls mean lower latency times and fewer moving parts. UTCP also avoids forcing every tool into an HTTP proxy model that may not fit its underlying architecture. The tool itself remains responsible for its own security and access controls, eliminating the need for centralized credential storage or relay services.

The main disadvantage of UTCP is that more responsibility shifts to the AI client, which must understand multiple transport mechanisms and handle authentication, retries, and error conditions without relying on a uniform execution layer. This trade-off favors architectural flexibility and performance over the operational simplicity of proxy-based systems.

Example: UTCP Protocol JSON

Here is an example of a UTCP tool description that instructs an AI agent how to call a weather API directly.

{
  "tools": [
    {
      "name": "get_weather",
      "description": "Fetch current weather for a city",
      "tool_call_template": {
        "call_template_type": "http",
        "url": "https://api.weather.example/v1/current",
        "http_method": "GET",
        "parameters": {
          "city": {
            "type": "string",
            "required": true
          }
        }
      }
    }
  ]
}

Instead of acting like a relay, this document serves as a precise instruction set that the AI can follow to construct and execute the request on its own.

Comparing and Contrasting MCP and UTCP

When comparing MCP and UTCP, architectural differences are the most obvious distinction. MCP relies on a client-server-tool chain, while UTCP favors direct client-to-tool communication. This structural choice has consequences for performance, operational complexity, and flexibility. MCP generally simplifies client implementation at the cost of additional infrastructure and added latency. UTCP minimizes infrastructure requirements and execution overhead but requires more capable clients that can handle diverse protocols and authentication schemes.

UTCP also emphasizes native support for open standards and interoperability with existing API descriptions. UTCP extends the widely used OpenAPI specification to include agent-focused metadata and execution instructions, for example, while remaining fully backward-compatible with standard OpenAPI documents.

This means existing OpenAPI definitions can be automatically converted into UTCP manuals without changing the underlying APIs, enabling AI agents to discover and directly invoke REST, gRPC, WebSocket, CLI, and other native interfaces. MCP’s model defines a consistent proxy-based interface that tools must adopt. On the other hand, it improves uniformity at the expense of extensibility.

From a performance standpoint, UTCP tends to outperform MCP in scenarios that are sensitive to latency because it avoids proxies. As far as ecosystems are concerned, MCP is currently more popular, has more vendor backing, and has improved interoperability. UTCP excels in environments where tools already exist and exposing them directly is preferable to wrapping them in yet another service layer.

MCP vs. UTCP: The Future of Agentic Tool-Calling

MCP and UTCP represent two coherent but divergent philosophies for AI tool calling. MCP prioritizes standardization and abstraction, making it easier for AI systems to interact with tools through a single, well-defined interface. UTCP prioritizes directness and efficiency, standardizing how tools are described while preserving their native execution paths. Neither approach is universally superior. Each one reflects a different assumption about infrastructure, performance, and developer responsibility.

Based on these trends, it seems likely that both protocols will continue to develop in parallel. MCP may deepen its role as a unifying standard backed by large AI platforms, while UTCP may gain traction among teams seeking minimal overhead and maximal flexibility. Hybrid approaches, where UTCP-described tools are surfaced through MCP servers or where clients support both models, also seem likely. As AI systems increasingly act rather than merely respond, the way they call tools will remain a foundational design choice rather than an implementation detail.