In February 2026, Peter Steinberger, the creator of OpenClaw, told Lex Fridman that the best tool for agentic AI has been on their desktop for 50 years. According to him, the simple command line interface (CLI) is the ideal tool for working with today’s non-deterministic technologies. The modular architecture of AI, LLMs, and agents require thinking outside of the monolith.

Agentic AI has been one of the hottest topics in tech of 2026, with 65% of enterprises already using AI agents, and 81% of these enterprises planning on expanding its usage. Considering the interest, it’s little surprise that agentic protocols have been dominating the headlines this year.

This leaves the rest of us to sort out the reality from the hype when deciding which tool to use for connecting AI agents with data sources and APIs. As the most widely adoped agentic protocol, Model Context Protocol (MCP) is often referred to as a universal standard, which is fitting given its aspirations to be a USB-C for AI. However, is MCP really the best, most efficient method for AI agent tooling? Or is legacy technology like the CLI still the better bet? To find out, we’ve done a side-by-side comparison of MCP versus CLI to help you make up your mind.

MCP vs. CLI: A Side-by-Side Comparison

Although MCP and CLI are often mentioned in the same breath, both technologies are quite different even though they’re sometimes used for the same purpose. The command line interface is where humans are able to insert prompts to instruct a system to do something, on one hand. MCP is a specification for machines to communicate with one another that is designed specifically for AI agents. Both specifications allow machines to perform an action by inputting structured data. Both MCP and CLI are able to be chained together into complex workflows, as well.

While both approaches have a similar goal, how they get there is remarkably different. The CLI is meant as a human-machine interface, which means the agent needs to stand in for the actual user. Interestingly, the main advantage of using CLIs for API interaction with AI could be related to the model itself. Since large language models (LLMs) tend to be trained on unstructured user data collected off the internet, is has far more examples of user-generated input and CLI commands than the abstract commands that MCP works with.

CLI is not without its drawbacks, though. CLI output is usually returned as raw text and numbers, for one thing. That output needs to be re-interpreted by the agent when it’s received, making it slightly less efficient than MCP in some circumstances. CLI’s main drawback, however, is how it handles authorization. CLI essentially acts as a stand-in for the user, so authorization solutions aren’t a priority. If your agent is going to have multiple users or be interacting primarily with machines, MCP might be a better pick.

MCP is designed to be used by machines across the entire workflow, however. Instead of issuing raw commands, an agentic AI using MCP will assess a command, invoke the proper tools, and then execute the actions. MCP is not without its drawbacks either, though, as every tool the agentic system will use needs to be made into an MCP server. It also adds overhead to every interaction. It does make MCP more re-usable, however, as it standardizes interactions across both tools and services.

Benchmarking MCP and CLI

Performance is the main reason developers have been comparing MCP and CLI, with advocates for both sides stating their strategy is better. Some claim that CLI outperforms MCP, while others claim that MCP is superior. A recent benchmarking study by Smithery ran 756 benchmark tests with three different models: Claude Haiku 4.5, GPT 5.4, and Claude Sonnet 4.6. Each of these models used three APIs: GitHub REST, Linear GraphQL, and Singapore Bus REST. Each test used at least two API calls to measure each model’s ability to successfully complete a complex workflow. According to Smithery’s benchmark, MCP has a higher success rate when calling the GitHub REST API, successfully completing 91.7% of calls to the GitHub REST API compared to 83.3% from the CLI. It also only used 28.8k tokens, compared to 82.9k tokens by the CLI. MCP had a lower latency time, as well, with a median latency of 10.4s compared to CLI’s 24.9s.

Source: Smithery CLI Vs. MCP Benchmark Study

MCP performed even more impressively at scale, successfully completing 100% of calls using all 826 tools in the GitHub repository compared to 87.5% using CLI + descriptions. It also uses fewer tokens, at 76,101 median tokens compared to 79,375 tokens with CLI. It also has a lower latency, with a 21.4s latency time, compared to 26.1s latency with CLI.

Source: Smithery CLI Vs. MCP Benchmark Study

The high success rate of MCP is partially due to its workflow. For certain situations, MCP only needs to call a single tool as opposed to CLI, which has to invoke five steps to achieve the same result.

Drilling down deeper into Smithery’s CLI versus MCP benchmark study reveals that their comparison is less about the specifications themselves and more about description and extensibility. Giving an agent access to a raw API only has a 53% success rate for example. Giving the agent access to the API specifications raises the success rate to 75.8%. Providing an agent access to the CLI and a search tool increases the success rate of 87.5%, almost as high as MCP, which has an average of 91.7%.

Source: Smithery CLI Vs. MCP Benchmark Study

Of course, it’s not so cut-and-dry to simply state that one approach is “better” than another. A different benchmark study from Port of Context found that CLI is cheaper and more efficient than MCP in certain circumstances. A simple task completed with CLI only requires 3,001 tokens compared to 19,172 tokens from MCP due to the protocol having to inject the entire schema each time. In less straightforward transactions, MCP still outperforms CLI, though. Raw MCP consumed 506,970 tokens, or an average of 42,248 tokens per task, to successfully complete 12 tasks compared to 711,555 tokens, or 59,296 tokens for CLI.

When to Use MCP vs. CLI for AI Agents

As is usually the case with new tools, comparing MCP vs. CLI is less of an either/or and more of a yes/and. Carefully reading over Smithery’s CLI vs. MCP benchmark reveals that MCP is great for efficiently performing tasks due to the agent’s ability to find and use the correct tool. CLI can perform almost as well when the agent’s provided with a description of the API it’s using or a search tool. CLI also works best with familiar APIs like the GitHub REST API. If you’re using more obscure APIs, make sure to provide the agentic AI system with API descriptions, specifications, and access to a search tool. Proper security will be non-negligible for agentic AI and MCP as well.

There are still situations where CLI is preferable, though. If you’re working with agentic AI for local systems, CLI is the way to go. As Smithery itself put it, “For local tools like git, docker, or ffmpeg, CLI is the native surface and MCP has no business replacing it. But for remote services, especially large internal APIs with zero training priors, or for security-sensitive workflows, a typed agent contract beats forcing every model through raw shell syntax.” It’s also preferable if you’re just using CLI for simple transactions, as shown by the Port of Context benchmark study.