Code2MCP: A Multi-Agent Framework for Automated Transformation of Code Repositories into Model Context Protocol Services

• URL: http://arxiv.org/abs/2509.05941v1
• Date: Sun, 07 Sep 2025 06:13:25 GMT
• Title: Code2MCP: A Multi-Agent Framework for Automated Transformation of Code Repositories into Model Context Protocol Services
• Authors: Chaoqian Ouyang, Ling Yue, Shimin Di, Libin Zheng, Shaowu Pan, Min-Ling Zhang,
• Abstract summary: This paper introduces Code2MCP, a highly automated framework designed to transform any GitHub repository into a functional MCP service.<n>A key innovation of our framework is an LLM-driven, closed-loop "Run--Review--Fix" cycle, which enables the system to autonomously debug and repair the code it generates.
• Score: 49.5217775646447
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The proliferation of Large Language Models (LLMs) has created a significant integration challenge in the AI agent ecosystem, often called the "$N \times M$ problem," where N models require custom integrations for M tools. This fragmentation stifles innovation and creates substantial development overhead. While the Model Context Protocol (MCP) has emerged as a standard to resolve this, its adoption is hindered by the manual effort required to convert the vast universe of existing software into MCP-compliant services. This is especially true for the millions of open-source repositories on GitHub, the world's largest collection of functional code. This paper introduces Code2MCP, a highly automated, agentic framework designed to transform any GitHub repository into a functional MCP service with minimal human intervention. Our system employs a multi-stage workflow that automates the entire process, from code analysis and environment configuration to service generation and deployment. A key innovation of our framework is an LLM-driven, closed-loop "Run--Review--Fix" cycle, which enables the system to autonomously debug and repair the code it generates. Code2MCP produces not only deployable services but also comprehensive technical documentation, acting as a catalyst to accelerate the MCP ecosystem by systematically unlocking the world's largest open-source code repository and automating the critical last mile of tool integration. The code is open-sourced at https://github.com/DEFENSE-SEU/MCP-Github-Agent.

Related papers

• From Tool Orchestration to Code Execution: A Study of MCP Design Choices [20.817336331051752]
  Model Context Protocols (MCPs) provide a unified platform for agent systems to discover, select, and orchestrate tools across heterogeneous execution environments.<n>Recent MCP designs have incorporated code execution as a first-class capability, an approach called Code Execution MCP (CEMCP)<n>This work formalizes the architectural distinction between context-coupled (traditional) and context-decoupled (CEMCP) models, analyzing their fundamental scalability trade-offs.
  arXiv  Detail & Related papers  (2026-02-17T19:03:08Z)
• MCP-Flow: Facilitating LLM Agents to Master Real-World, Diverse and Scaling MCP Tools [58.5971352939562]
  Large Language Models increasingly rely on external tools to perform complex, realistic tasks.<n>Existing MCP research covers few servers, depends on costly manual curation, and lacks training support.<n>We introduce MCP-Flow, an automated web-agent-driven pipeline for large-scale server discovery, data synthesis, and model training.
  arXiv  Detail & Related papers  (2025-10-28T10:42:17Z)
• LiveMCPBench: Can Agents Navigate an Ocean of MCP Tools? [50.60770039016318]
  We present LiveMCPBench, the first comprehensive benchmark for benchmarking Model Context Protocol (MCP) agents.<n>LiveMCPBench consists of 95 real-world tasks grounded in the MCP ecosystem.<n>Our evaluation covers 10 leading models, with the best-performing model reaching a 78.95% success rate.
  arXiv  Detail & Related papers  (2025-08-03T14:36:42Z)
• AgentMesh: A Cooperative Multi-Agent Generative AI Framework for Software Development Automation [0.0]
  We propose a Python-based framework that uses multiple cooperating LLM-powered agents to automate software development tasks.<n>In AgentMesh, specialized agents - a Planner, Coder, Debugger, and Reviewer - work in concert to transform a high-level requirement into fully realized code.
  arXiv  Detail & Related papers  (2025-07-26T10:10:02Z)
• Making REST APIs Agent-Ready: From OpenAPI to Model Context Protocol Servers for Tool-Augmented LLMs [0.0]
  We present AutoMCP, a compiler that generates MCP servers from OpenAPI 2.0/3.0 specifications.<n>We evaluate AutoMCP on 50 real-world APIs spanning 5,066 endpoints across over 10 domains.
  arXiv  Detail & Related papers  (2025-07-21T20:20:31Z)
• MCPWorld: A Unified Benchmarking Testbed for API, GUI, and Hybrid Computer Use Agents [14.736516215309768]
  We propose MCPWorld, the first automatic CUA testbed for API, GUI, and API-GUI hybrid agents.<n>A key principle of MCPWorld is the use of "white-box apps", i.e., those with source code availability and can be revised/re-compiled as needed.<n> MCPWorld includes 201 well curated and annotated user tasks, covering diversified use cases and difficulty levels.
  arXiv  Detail & Related papers  (2025-06-09T11:50:33Z)
• MAS-ZERO: Designing Multi-Agent Systems with Zero Supervision [76.42361936804313]
  We introduce MAS-ZERO, the first self-evolved, inference-time framework for automatic MAS design.<n> MAS-ZERO employs meta-level design to iteratively generate, evaluate, and refine MAS configurations tailored to each problem instance.
  arXiv  Detail & Related papers  (2025-05-21T00:56:09Z)
• AutoP2C: An LLM-Based Agent Framework for Code Repository Generation from Multimodal Content in Academic Papers [9.851681616116718]
  We introduce Paper-to-Code'' (P2C), a novel task that transforms the multimodal content of scientific publications into fully executable code repositories.<n>We propose AutoP2C, a multi-agent framework based on large language models that processes both textual and visual content from research papers to generate complete code repositories.
  arXiv  Detail & Related papers  (2025-04-28T05:47:37Z)
• Paper2Code: Automating Code Generation from Scientific Papers in Machine Learning [70.04746094652653]
  We introduce PaperCoder, a framework that transforms machine learning papers into functional code repositories.<n>PaperCoder operates in three stages: planning, designs the system architecture with diagrams, identifies file dependencies, and generates configuration files.<n>We then evaluate PaperCoder on generating code implementations from machine learning papers based on both model-based and human evaluations.
  arXiv  Detail & Related papers  (2025-04-24T01:57:01Z)
• MCP Bridge: A Lightweight, LLM-Agnostic RESTful Proxy for Model Context Protocol Servers [0.5266869303483376]
  MCP Bridge is a lightweight proxy that connects to multiple MCP servers and exposes their capabilities through a unified API.<n>The system implements a risk-based execution model with three security levels standard execution, confirmation, and Docker isolation while maintaining backward compatibility with standard MCP clients.
  arXiv  Detail & Related papers  (2025-04-11T22:19:48Z)
• LLM-Generated Microservice Implementations from RESTful API Definitions [3.740584607001637]
  This paper presents a system that uses Large Language Models (LLMs) to automate the API-first development of software.<n>The system generates OpenAPI specification, generating server code from it, and refining the code through a feedback loop that analyzes execution logs and error messages.<n>The system has the potential to benefit software developers, architects, and organizations to speed up software development cycles.
  arXiv  Detail & Related papers  (2025-02-13T20:50:33Z)
• ExecRepoBench: Multi-level Executable Code Completion Evaluation [45.963424627710765]
  We introduce a novel framework for enhancing code completion in software development through the creation of a repository-level benchmark ExecRepoBench.<n>We present a multi-level grammar-based completion methodology conditioned on the abstract syntax tree to mask code fragments at various logical units.<n>Then, we fine-tune the open-source LLM with 7B parameters on Repo-Instruct to produce a strong code completion baseline model Qwen2.5-Coder-Instruct-C.
  arXiv  Detail & Related papers  (2024-12-16T17:14:35Z)
• MOSS: Enabling Code-Driven Evolution and Context Management for AI Agents [7.4159044558995335]
  We introduce MOSS (llM-oriented Operating System Simulation), a novel framework that integrates code generation with a dynamic context management system. At its core, the framework employs an Inversion of Control container in conjunction with decorators to enforce the least knowledge principle. We show how this framework can enhance the efficiency and capabilities of agent development and highlight its advantages in moving towards Turing-complete agents.
  arXiv  Detail & Related papers  (2024-09-24T14:30:21Z)
• AgentScope: A Flexible yet Robust Multi-Agent Platform [66.64116117163755]
  AgentScope is a developer-centric multi-agent platform with message exchange as its core communication mechanism. The abundant syntactic tools, built-in agents and service functions, user-friendly interfaces for application demonstration and utility monitor, zero-code programming workstation, and automatic prompt tuning mechanism significantly lower the barriers to both development and deployment.
  arXiv  Detail & Related papers  (2024-02-21T04:11:28Z)
• CodeTF: One-stop Transformer Library for State-of-the-art Code LLM [72.1638273937025]
  We present CodeTF, an open-source Transformer-based library for state-of-the-art Code LLMs and code intelligence. Our library supports a collection of pretrained Code LLM models and popular code benchmarks. We hope CodeTF is able to bridge the gap between machine learning/generative AI and software engineering.
  arXiv  Detail & Related papers  (2023-05-31T05:24:48Z)
• OmniForce: On Human-Centered, Large Model Empowered and Cloud-Edge Collaborative AutoML System [85.8338446357469]
  We introduce OmniForce, a human-centered AutoML system that yields both human-assisted ML and ML-assisted human techniques. We show how OmniForce can put an AutoML system into practice and build adaptive AI in open-environment scenarios.
  arXiv  Detail & Related papers  (2023-03-01T13:35:22Z)

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.