ToolRosetta: Bridging Open-Source Repositories and Large Language Model Agents through Automated Tool Standardization

• URL: http://arxiv.org/abs/2603.09290v1
• Date: Tue, 10 Mar 2026 07:19:43 GMT
• Title: ToolRosetta: Bridging Open-Source Repositories and Large Language Model Agents through Automated Tool Standardization
• Abstract summary: ToolRosetta is a framework that automatically translates open-source code repositories and APIs into MCP-compatible tools.<n>Given a user task, ToolRosetta autonomously plans toolchains, identifies relevant toolchains, and converts them into executable MCP services.
• Score: 51.92237664440418
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reusing and invoking existing code remains costly and unreliable, as most practical tools are embedded in heterogeneous code repositories and lack standardized, executable interfaces. Although large language models (LLMs) and Model Context Protocol (MCP)-based tool invocation frameworks enable natural language task execution, current approaches rely heavily on manual tool curation and standardization, which fundamentally limits scalability. In this paper, we propose ToolRosetta, a unified framework that automatically translates open-source code repositories and APIs into MCP-compatible tools that can be reliably invoked by LLMs. Given a user task, ToolRosetta autonomously plans toolchains, identifies relevant codebases, and converts them into executable MCP services, enabling end-to-end task completion with minimal human intervention. In addition, ToolRosetta incorporates a security inspection layer to mitigate risks inherent in executing arbitrary code. Extensive experiments across diverse scientific domains demonstrate that ToolRosetta can automatically standardize a large number of open-source tools and reduce the human effort required for code reproduction and deployment. Notably, by seamlessly leveraging specialized open-source tools, ToolRosetta-powered agents consistently improve task completion performance compared to commercial LLMs and existing agent systems.

Related papers

• Bridging Thought and Action: Taming Long-Horizon Instability in Open-Source LLM Agents with a MetaTool-Enhanced ROS Framework [0.0]
  Large Language Models (LLMs) have enabled more natural human-robot interaction, but open-source models exhibit unstable long-horizon reasoning.<n>This paper presents an enhanced ROS-Agent based architecture that improves task reliability and execution efficiency.
  arXiv  Detail & Related papers  (2026-09-11T10:00:49Z)
• Gecko: A Simulation Environment with Stateful Feedback for Refining Agent Tool Calls [56.407063247662336]
  We introduce Gecko, a comprehensive environment that simulates tool responses using a combination of rules and LLMs.<n>GATS consistently improves the tool calling performance of various LLMs including GPT-4o, GPT-5, and Gemini-3.0-pro.
  arXiv  Detail & Related papers  (2026-02-22T15:02:00Z)
• SAGE: Tool-Augmented LLM Task Solving Strategies in Scalable Multi-Agent Environments [2.071720670587172]
  We present SAGE, a specialized conversational AI interface based on the OPACA framework for tool discovery and execution.<n>We implement a number of task-solving strategies, making use of agentic concepts and prompting methods in various degrees of complexity.<n>The results are promising and highlight the distinct strengths and weaknesses of different task-solving strategies.
  arXiv  Detail & Related papers  (2026-01-12T15:49:47Z)
• DeepAgent: A General Reasoning Agent with Scalable Toolsets [111.6384541877723]
  DeepAgent is an end-to-end deep reasoning agent that performs autonomous thinking, tool discovery, and action execution.<n>To address the challenges of long-horizon interactions, we introduce an autonomous memory folding mechanism that compresses past interactions into structured episodic, working, and tool memories.<n>We develop an end-to-end reinforcement learning strategy, namely ToolPO, that leverages LLM-simulated APIs and applies tool-call advantage attribution to assign fine-grained credit to the tool invocation tokens.
  arXiv  Detail & Related papers  (2025-10-24T16:24:01Z)
• Generalizable End-to-End Tool-Use RL with Synthetic CodeGym [52.31172214690965]
  We introduce CodeGym, a framework that synthesizes diverse, verifiable, and controllable multi-turn tool-use environments for agent RL.<n>CodeGym rewrites static coding problems into interactive environments by extracting atomic functions or logic into callable tools.<n>Models of varying sizes and chain-of-thought configurations, trained in CodeGym, exhibit consistent out-of-distribution generalizability.
  arXiv  Detail & Related papers  (2025-09-22T03:03:56Z)
• LLM Agents Making Agent Tools [2.5529148902034637]
  Tool use has turned large language models (LLMs) into powerful agents that can perform complex multi-step tasks.<n>But these tools must be implemented in advance by human developers.<n>We propose ToolMaker, an agentic framework that autonomously transforms papers with code into LLM-compatible tools.
  arXiv  Detail & Related papers  (2025-02-17T11:44:11Z)
• ToolCoder: A Systematic Code-Empowered Tool Learning Framework for Large Language Models [81.12673534903979]
  Tool learning has emerged as a crucial capability for large language models (LLMs) to solve complex real-world tasks through interaction with external tools.<n>We propose ToolCoder, a novel framework that reformulates tool learning as a code generation task.
  arXiv  Detail & Related papers  (2025-02-17T03:42:28Z)
• Tool Learning in the Wild: Empowering Language Models as Automatic Tool Agents [56.822238860147024]
  Augmenting large language models with external tools has emerged as a promising approach to extend their utility.<n>Previous methods manually parse tool documentation and create in-context demonstrations, transforming tools into structured formats for LLMs to use in their step-by-step reasoning.<n>We propose AutoTools, a framework that enables LLMs to automate the tool-use workflow.
  arXiv  Detail & Related papers  (2024-05-26T11:40:58Z)
• EASYTOOL: Enhancing LLM-based Agents with Concise Tool Instruction [56.02100384015907]
  EasyTool is a framework transforming diverse and lengthy tool documentation into a unified and concise tool instruction. It can significantly reduce token consumption and improve the performance of tool utilization in real-world scenarios.
  arXiv  Detail & Related papers  (2024-01-11T15:45:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.