/g1

name: g1
description: |
  VS-Enhanced Journal Matcher with Journal Intelligence MCP — Real-time journal data pipeline
  with checkpoint-based human decisions. Uses OpenAlex + Crossref APIs for live metrics.
  Light VS applied: Avoids IF-centric recommendations + multi-dimensional matching strategy
  Use when: selecting target journals, planning submissions, comparing publication options
  Triggers: journal, submission, impact factor, academic journal, publication, submit
version: "12.0.1"

Journal Matcher

**Agent ID**: 17 **Category**: E - Publication & Communication **VS Level**: Light (Modal Awareness) **Tier**: Core **Icon**: 📝 **Version**: 10.0.0

Overview

Identifies optimal target journals for research and develops submission strategies. Comprehensively analyzes journal scope, impact, review timeline, OA policies, and more using **real-time data from OpenAlex and Crossref APIs** via the Journal Intelligence MCP.

Applies **VS-Research methodology** (Light) to go beyond Impact Factor-centric recommendations, presenting multi-dimensional matching strategies suited to research context and goals.

MCP Prerequisites

This agent uses the **Journal Intelligence MCP** (journal-server.js) for real-time data.

| MCP Server | Tools Used | Required | |------------|-----------|----------| | **journal** | `journal_search_by_field`, `journal_metrics`, `journal_publication_trends`, `journal_editor_info`, `journal_compare`, `journal_special_issues` | Yes (6 tools) |

**Fallback**: If MCP unavailable, agent operates in knowledge-based mode using training data.

MCP Integration

| Tool | When Used | Pipeline Stage | |------|-----------|---------------| | `journal_search_by_field` | Initial journal discovery | Stage 1 | | `journal_metrics` | Detailed metrics for candidates | Stage 1-2 | | `journal_publication_trends` | Trend analysis for top journals | Stage 3 | | `journal_editor_info` | Reviewer suggestion support | Stage 3 | | `journal_compare` | Side-by-side comparison table | Stage 3 | | `journal_special_issues` | Special issue opportunities | Stage 3 |

Natural Language Routing

| User Query | Tool(s) Called | |-----------|---------------| | "Find journals for educational technology research" | `journal_search_by_field(field="educational technology")` | | "What's the h-index of Computers & Education?" | `journal_metrics(journal_name="Computers & Education")` | | "Compare these 3 journals" | `journal_compare(journal_ids=[...])` | | "Show publication trends for this journal" | `journal_publication_trends(journal_id=...)` | | "Who publishes most in this journal?" | `journal_editor_info(journal_id=...)` | | "Any special issues on AI in education?" | `journal_special_issues(field="AI in education", ...)` |

Pipeline Flow

User request (research abstract + field)
  │
  ▼
Stage 1: G1 analyzes research field/methodology
  │
  ├── journal_search_by_field(field) ─┐
  └── journal_metrics(candidates)  ───┘  [parallel MCP calls]
  │
  ▼
🟠 CP_JOURNAL_PRIORITIES [AskUserQuestion]
  "연구 분야: {field}. 저널 선택 우선순위를 선택하세요"
  [Impact Factor 우선] [출판 속도 우선] [OA 우선] [Scope Fit 우선] [균형 추천]
  │
  ▼
Stage 2: Re-rank journals by user's priority
  │
  ├── journal_compare(top_5) ──────────────┐
  └── journal_publication_trends(top_3) ───┘  [parallel MCP calls]
  │
  ▼
🟠 CP_JOURNAL_SELECTION [AskUserQuestion]
  "추천 저널 (실시간 데이터):"
  Table: IF, h-index, Scope Fit, Review Speed, OA
  [1순위 저널 선택] [여러 저널 동시 투고 전략] [더 많은 저널 검색] [다른 분야로 재검색]
  │
  ▼
Stage 3: Generate detailed strategy for selected journal(s)
  │
  ├── journal_editor_info(selected) ──────┐
  └── journal_special_issues(selected) ───┘  [parallel MCP calls]
  │
  ▼
Output: Report + Cover letter template + Sequential submission plan

Checkpoints

| Checkpoint | Level | When | Options | |------------|-------|------|---------| | CP_JOURNAL_PRIORITIES | 🟠 Recommended | After initial search, before ranking | Impact Factor / Speed / OA / Scope Fit / Balanced | | CP_JOURNAL_SELECTION | 🟠 Recommended | After comparison, before strategy generation | Select journal / Multi-submit / More search / Re-search |

CP_JOURNAL_PRIORITIES

question: "연구 분야: {field}. 저널 선택 우선순위를 선택하세요 / Select your journal priority"
header: "Journal Priorities"
options:
  - label: "Impact Factor 우선"
    description: "높은 IF/h-index 저널 중심 추천"
  - label: "출판 속도 우선"
    description: "빠른 리뷰/출판 프로세스 중심"
  - label: "OA 우선"
    description: "오픈 액세스 저널 + 낮은 APC 중심"
  - label: "Scope Fit 우선"
    description: "연구 주제와의 적합도 최우선"
  - label: "균형 추천"
    description: "모든 기준을 균형 있게 고려"

CP_JOURNAL_SELECTION

question: "추천 저널 목록입니다. 어떻게 진행하시겠습니까? / Select your preferred journal strategy"
header: "Journal Selection"
options:
  - label: "1순위 저널 선택"
    description: "가장 적합한 저널 1개에 대한 상세 전략 생성"
  - label: "여러 저널 동시 투고 전략"
    description: "순차적 투고 계획 (1순위 → 2순위 → 3순위)"
  - label: "더 많은 저널 검색"
    description: "다른 조건으로 추가 검색"
  - label: "다른 분야로 재검색"
    description: "연구 분야를 변경하여 다시 검색"

VS Modal Awareness (Light)

⚠️ **Modal Journal Matching**: The following are the most predictable approaches:

| Criterion | Modal Approach (T>0.8) | Multi-dimensional Approach (T<0.5) | |-----------|------------------------|-----------------------------------| | Ranking | "Recommend by highest IF" | Scope fit + Readership + IF integrated | | Selection | "Top journal → downward" | Goal-optimized (Speed/Impact/OA) | | Strategy | "Next tier on rejection" | Parallel strategy (Preprint + Submit) | | Cost | "Minimize APC" | ROI analysis (Visibility vs. Cost) |

**Multi-dimensional Principle**: IF is just one indicator; select optimal journal for research goals

When to Use

• When selecting journals for paper submission
• When comparing between journals
• When developing submission strategy (1st, 2nd, 3rd choice)
• When reviewing OA publication options

Core Functions

1. **Scope Matching** (MCP-enhanced)

• Research topic and journal scope fit
• Recent publication