Gate for AI Double-layer Architecture: How MCP and Skills Achieve Zero-Code Quantitative Trading

The crypto market has entered the era of native AI agents, where AI is no longer a passive information query tool but capable of completing the full trading cycle from research to execution. The core driving force behind this transformation is the upgrade of underlying infrastructure—In March 2026, Gate officially launched Gate for AI, which, through a dual-layer architecture of MCP and Skills, fully protocolizes and opens exchange capabilities, enabling AI Agents to participate in real market trading for the first time.

As a key deployment of Gate’s Intelligent Web3 strategy, Gate for AI has covered over 80 application scenarios, expanded MCP tools to 161 items, and surpassed 10,000 strategies in the Skills Hub, with GateRouter integrating more than 20 mainstream large models.

MCP Standardized Tool Interface: The Protocol Layer Connecting AI and the Trading World

MCP, or Model Context Protocol, was proposed by Anthropic in November 2024 and has now become the data standard for connecting large language models with external tools. In crypto trading scenarios, MCP’s core value lies in “standardization”—it encapsulates fundamental operations such as market data queries, account management, order execution, and on-chain data reading into a unified protocol interface, allowing any MCP-compatible AI model to be plug-and-play.

On February 2, 2026, Gate completed the packaging and validation of its first MCP Tools, becoming the world’s first trading platform to launch MCP Tools. The initial 17 tools cover core data capabilities in spot and derivatives markets, including order book depth, funding rates, liquidation order history, and risk indicators. Since then, MCP tools have continued to expand to 161 items, covering four major dimensions: market data, trading, accounts, and on-chain data.

Notably, Gate for AI opens five major capability domains via MCP within the same interface system: centralized trading, on-chain trading, wallet and signature systems, real-time news and market intelligence, and on-chain data and industry information queries. The combination of these five domains means AI is no longer just a tool for executing single commands but can complete a full “research—judgment—execution—monitoring” cycle, functioning as an elementary trader.

In short, MCP solves the problem of whether AI can use exchange tools. It lowers the access barrier, making Gate one of the default infrastructures in the AI ecosystem.

Skills Pre-Arranged Advanced Capability Modules: From “Usable” to “Smarter Use”

If MCP is a standardized tool interface, then Skills is a strategy engine built on top of MCP. The introduction of Skills marks the evolution of AI capability systems from “tool-level invocation” to “task-level orchestration”—it addresses how AI can use these tools more intelligently.

Skills is essentially a set of pre-arranged advanced capability modules that package professional market strategies into “skill packs” that AI can directly invoke. A Skill is not just a prompt but a structured knowledge module containing context, best practices, and specific tool combinations. Currently, Skills modules cover key areas such as market opportunity scanning, position entry evaluation, arbitrage opportunity detection, and risk analysis.

In practical operation, the logic for invoking Skills is: when a user asks in natural language, the AI calls the corresponding Skills combination—for example, “arbitrage detection” plus “risk analysis”—automatically completing data analysis and judgment, ultimately outputting a structured report or executing trades. All calls to Skills modules operate within Gate’s existing risk control framework, ensuring AI actions are performed within safe and controllable boundaries.

Skills Hub is the aggregation and distribution center for Skills strategies. After a comprehensive upgrade in March 2026, the number of AI skills expanded from 11 to over 10,000, covering core scenarios such as market analysis, arbitrage strategies, trade execution, and risk management. The Hub introduces an 8-category system with tagging and filtering mechanisms, combined with multi-dimensional search and intelligent ranking capabilities, helping users quickly locate target strategies.

MCP and Skills Collaborative Logic

MCP and Skills do not operate in isolation but form a “protocol layer + capability layer” dual-layer collaborative architecture. MCP handles broad coverage and easy access, encapsulating the fundamental operations of the five major capability domains; Skills builds on this to perform high-level orchestration, packaging multiple data sources and logical models into callable strategy modules.

For example, in a BTC breakout position: MCP provides price queries, order submission, account management, and other basic tools; Skills packages “market scanning” and “position entry evaluation” into a strategy module. When the AI receives a user’s natural language command, it sequentially calls MCP tools to fetch real-time data, invokes the Skills module for analysis and judgment, and finally executes the trade via MCP interface. The collaboration between MCP and Skills enables AI to evolve from a “passive query” to an “active execution” intelligent trading assistant.

Zero-Code AI Quantitative Trading Workbench: From Intent to Execution Paradigm Shift

One of the direct outcomes of the MCP and Skills dual-layer architecture is Gate’s zero-code AI quantitative trading workbench. This platform shifts the creation of quantitative strategies from “code-driven” to “intent-driven.” Users do not need to write any code; simply describe trading logic in everyday language, and the system automatically generates complete, executable strategy code, performs backtesting on historical data, and deploys live trading with one click.

For example, monitoring key BTC levels: users can input, “When BTC price breaks the 24-hour high and 1-hour trading volume significantly increases, establish an intelligent grid in spot, using 2,000 USDT, with an 8% stop loss.” The built-in AI will automatically fetch Gate’s real-time market data, calculate a safe price range based on recent average true range, recommend grid parameters suitable for high-volatility assets, and complete backtesting validation.

In traditional modes, traders manually gather market data, analyze trends, write strategies, and execute orders. With Gate for AI, these steps are automated and respond in real time to market changes. The strategy validation cycle shortens from “monthly” to “minute-level,” greatly reducing trial-and-error costs.

It’s worth noting that the zero-code AI quant platform and Skills Hub form a “strategy supply—strategy creation” dual-wheel drive: Skills Hub provides numerous validated strategy templates for one-click invocation, while the AI quant platform supports custom strategies generated via natural language. Together, they form a complete chain from strategy discovery to live deployment.

Trading Infrastructure in the Native Agent Era

The underlying logic of Gate for AI is to upgrade AI from a passive auxiliary tool to an autonomous agent with perception, reasoning, and action capabilities. The platform allows users to create or deploy personalized trading Agents that continuously operate in specific market scenarios—such as high-frequency trading in oscillating markets, trend-following in trending markets, or arbitrage opportunities based on on-chain data—executing automatically within user-authorized boundaries.

Gate for AI has built a complete invocation system of MCP + Skills + CLI, supporting direct participation of AI models in live trading, enabling strategy judgment to be efficiently translated into real trades. Strategically, Gate for AI is not merely adding a new feature module on top of existing services but transforming the entire exchange into an AI-native callable infrastructure layer, marking a shift from “interface product” to “AI-callable infrastructure” in crypto trading platforms.

Trend Outlook

2026 is widely regarded by the industry as the “Year of Agent Economy.” Messari predicts that by 2030, the AI Agent track will reach a market size of 30 trillion USD. The AI spending of large-scale cloud providers in the US alone is expected to exceed 650 billion USD by 2026. Under this trend, platforms providing standardized trading interfaces for AI Agents will become critical infrastructure in the machine economy era.

Gate for AI’s product layout clearly aligns with this trend. From the early launch of MCP Tools, to the successive release of Skills modules and zero-code AI quant platforms, and the milestone of over 10,000 strategies in Skills Hub, Gate is systematically building a complete trading infrastructure tailored for AI Agents.

Summary

The dual-layer architecture of Gate for AI, comprising MCP and Skills, essentially forms a capability invocation system that makes AI “both usable and smart in use.” MCP offers standardized tool interfaces for unified access across five major capability domains; Skills performs task-level orchestration, encapsulating professional strategies into reusable modules. Their collaboration turns zero-code quantitative trading from concept to reality—users can create and deploy strategies solely through natural language. As AI Agents continue to deepen their penetration into the crypto economy, the infrastructure built by Gate for AI is becoming a vital entry point into the Agent native era.