ToolACE-MCP: Generalizing History-Aware Routing from MCP Tools to the Agent Web

2026-01-13

AI Summary

This work addresses the scalability and generalization limitations of existing agent architectures in the face of exponentially growing tool ecosystems. The authors propose ToolACE-MCP, a lightweight routing agent endowed with historical awareness, which models multi-turn interaction trajectories through graph-based representations and learns a context-aware routing policy within the Model Context Protocol (MCP) framework. The approach enables plug-and-play deployment and zero-shot transfer to multi-agent collaborative settings, achieving significant advances in noise robustness and scalability over large candidate tool spaces. Experimental evaluation on the MCP-Universe and MCP-Mark real-world benchmarks demonstrates its superior capability for general-purpose orchestration in open-ended tool environments.

Technology Category

• Multiagent Systems: Modeling other Agents
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

With the rise of the Agent Web and Model Context Protocol (MCP), the agent ecosystem is evolving into an open collaborative network, exponentially increasing accessible tools. However, current architectures face severe scalability and generality bottlenecks. To address this, we propose ToolACE-MCP, a pipeline for training history-aware routers to empower precise navigation in large-scale ecosystems. By leveraging a dependency-rich candidate Graph to synthesize multi-turn trajectories, we effectively train routers with dynamic context understanding to create the plug-and-play Light Routing Agent. Experiments on the real-world benchmarks MCP-Universe and MCP-Mark demonstrate superior performance. Notably, ToolACE-MCP exhibits critical properties for the future Agent Web: it not only generalizes to multi-agent collaboration with minimal adaptation but also maintains exceptional robustness against noise and scales effectively to massive candidate spaces. These findings provide a strong empirical foundation for universal orchestration in open-ended ecosystems.

Problem

• Agent Web
• Model Context Protocol
• tool routing
• scalability
• generality

Innovation

• history-aware routing
• Model Context Protocol (MCP)
• Agent Web
• dependency-rich graph
• plug-and-play router