toolrosetta-tool-standardization

name	toolrosetta-tool-standardization
title	ToolRosetta: Automated Tool Standardization for LLM Agents
version	0.0.3
engine	skillxiv-v0.0.3-claude-opus-4.6
license	MIT
url	https://arxiv.org/abs/2603.09290
keywords	["Tool Standardization","MCP","LLM Agents","API Patterns","Tool Integration"]
description	Automate conversion of 630M+ heterogeneous GitHub repositories into standardized Model Context Protocol (MCP) services via hierarchical multi-agent system. Achieves 68.4% success rate after three repair cycles, 210s per repository versus 1589s manual; increases agent performance by 10.6–13.4% when integrated into existing systems. Use when scaling tool availability beyond manually curated sets.

Capability Gap

GitHub hosts over 630M repositories with practical tools embedded in heterogeneous code. Yet LLM agents operate with severely limited tool sets (5 tools in ToolFormer, 500+ in SciToolAgent). Manual standardization of each tool requires:

• Parsing code and dependencies
• Understanding function signatures and semantics
• Rewriting interfaces and designing schemas
• Creating executable wrappers

This scales infeasibly; only centralized platforms and large organizations can maintain tool catalogs.

Core Abstractions: Hierarchical Multi-Agent Architecture

# Hierarchical multi-agent system for tool standardization
class ToolRosettaSystem:
    """
    Four specialized agents orchestrate conversion of heterogeneous repos
    into standardized MCP services.
    """
    def __init__(self):
        self.tool_search_agent = ToolSearchAgent()
        self.mcp_construction_agent = MCPConstructionAgent()
        self.planning_agent = PlanningAgent()
        self.security_agent = SecurityAgent()

    def standardize_tool(self, tool_query, candidate_repos):
        """
        Pipeline: search → analyze → construct → secure → deploy.
        Achieves 68.4% success after three repair cycles.
        """
        # 1. Search: Semantic parsing + functional alignment assessment
        relevant_repos = self.tool_search_agent.retrieve_repos(
            tool_query, candidate_repos
        )

        # 2. Construct: Automated transformation pipeline
        mcp_services = []
        for repo in relevant_repos:
            service = self.mcp_construction_agent.transform(
                repo_path=repo.path,
                stages=[
                    "clone_repo",
                    "analyze_dependencies",
                    "configure_environment",
                    
                ]
            )
            mcp_services.append(service)

        
        execution_plan = .planning_agent.plan_workflow(
            tool_query, mcp_services
        )

        
        vetted_services = [
            svc  svc  mcp_services
             .security_agent.inspect(svc).is_safe
        ]

         execution_plan, vetted_services