ryanjoachim/mcp-batchit

** 단일 “batch execute” 요청으로 여러 MCP 도구가 호출하여 AI 에이전트에 대한 오버 헤드 및 토큰 사용을 감소시킵니다. **

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내용 표

1. 소개
2. 왜 BatchIt를 사용
3. 중요한 특징 & 한계
4. 임명 & 시작
5. 다 단계 사용법

• 구현 단계
• 정보 수집
• LLM‐Only Step (리스트 코드 정의)
• 문서 작성

6. 질문
7. 면허

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소개

⚠️NOTICE: 진행중인 작품 · 이 프로젝트는 여러 복잡한 과제를 해결하기 위해 적극적으로 개발되고 있습니다.

• 기존 MCP 서버와의 호환성 유지
• 다중 연결 클라이언트 (Cline, Roo, Claude Desktop)를 가진 수송 복잡성 해결
• 초보자 친화적 인 구현 만들기 · 기능적이지만 지속적인 개선과 변화를 기대합니다.

MCP BatchIt는 Model Context Protocol (MCP) 생태계의 간단한 집계 서버입니다. 단지 ** 하나 ** 도구: batch_execute. 여러 MCP 도구를 호출하는 것보다 (fetch, read_file], create_directory], write_file, 등) ** 메시지, 당신은 ** 배치 ** 그들과 함께 하나 개의 집계 요청.

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Table of Contents

1. Introduction
2. Why Use BatchIt
3. Key Features & Limitations
4. Installation & Startup
5. Multi-Phase Usage
   • Implementation Phases
     • Information Gathering
     • LLM‐Only Step (List Code Definitions)
     • Document Creation
6. FAQ
7. License

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Introduction

⚠️ NOTICE: Work in Progress

This project is actively being developed to address several complex challenges:

• Maintaining backwards compatibility with existing MCP servers
• Resolving transport complexities with multi-connection clients (Cline, Roo, Claude Desktop)
• Creating a beginner-friendly implementation

While functional, expect ongoing improvements and changes as we refine the solution.

MCP BatchIt is a simple aggregator server in the Model Context Protocol (MCP) ecosystem. It exposes just one tool: batch_execute. Rather than calling multiple MCP tools (like fetch, read_file, create_directory, write_file, etc.) in separate messages, you can batch them together in one aggregator request.

This dramatically reduces token usage, network overhead, and repeated context in your AI agent or LLM conversation.

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Why Use BatchIt

• One Action per Message Problem: Normally, an LLM or AI agent can only call a single MCP tool at a time, forcing multiple calls for multi-step tasks.

• Excessive Round Trips: 10 separate file operations might require 10 messages → 10 responses.

• BatchIt’s Approach:

  1. Takes a single batch_execute request.
  2. Spawns (or connects to) the actual target MCP server (like a filesystem server) behind the scenes.
  3. Runs each sub-operation (tool call) in parallel up to maxConcurrent.
  4. If one sub-op fails and stopOnError is true, it halts new sub-ops.
  5. Returns one consolidated JSON result.

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Key Features & Limitations

Features

1. Single “Batch Execute” Tool

   • You simply specify a list of sub‐ops referencing your existing MCP server’s tools.

2. Parallel Execution

   • Run multiple sub-ops at once, controlled by maxConcurrent.

3. Timeout & Stop on Error

   • Each sub-op races a timeoutMs, and you can skip remaining ops if one fails.

4. Connection Caching

   • Reuses the same connection to the downstream MCP server for repeated calls, closing after an idle timeout.

Limitations

1. No Data Passing Mid-Batch