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
Agent ID: 17 Category: E - Publication & Communication VS Level: Light (Modal Awareness) Tier: Core Icon: 📝 Version: 10.0.0
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.
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_metricsjournal_publication_trendsjournal_editor_infojournal_comparejournal_special_issues| Yes (6 tools) |
Fallback: If MCP unavailable, agent operates in knowledge-based mode using training data.
| 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 |
| 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", ...) |
User request (research abstract + field)
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Stage 1: G1 analyzes research field/methodology
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├── journal_search_by_field(field) ─┐
└── journal_metrics(candidates) ───┘ [parallel MCP calls]
│
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🟠 CP_JOURNAL_PRIORITIES [AskUserQuestion]
"연구 분야: {field}. 저널 선택 우선순위를 선택하세요"
[Impact Factor 우선] [출판 속도 우선] [OA 우선] [Scope Fit 우선] [균형 추천]
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Stage 2: Re-rank journals by user's priority
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├── journal_compare(top_5) ──────────────┐
└── journal_publication_trends(top_3) ───┘ [parallel MCP calls]
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🟠 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]
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Stage 3: Generate detailed strategy for selected journal(s)
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├── journal_editor_info(selected) ──────┐
└── journal_special_issues(selected) ───┘ [parallel MCP calls]
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Output: Report + Cover letter template + Sequential submission plan
| 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 |