# Pubmed Fulltext Access

> Full text access using get_fulltext, figure extraction, and institutional link tools. Triggers: 全文, fulltext, PDF, open access, 免費下載, PMC, 開放取用

- Skill: `u9401066-pubmed-search-mcp/pubmed-fulltext-access` (Agent Skill)
- Install (CLI): `npx skillmds add u9401066-pubmed-search-mcp/pubmed-fulltext-access`
- Raw SKILL.md: https://api.skillmd.com/api/skills/u9401066-pubmed-search-mcp/pubmed-fulltext-access/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Docs & Writing
- Author: u9401066 (https://skillmd.com/u/u9401066-pubmed-search-mcp)
- Updated: 2026-09-10
- Page: https://skillmd.com/skills/u9401066-pubmed-search-mcp/pubmed-fulltext-access

---


# 全文取得指南

## 描述
目前的全文 workflow 以 `get_fulltext` 為核心。它會自動嘗試 Europe PMC、Unpaywall、CORE，必要時可擴展到更多來源。若需要圖表、文字探勘或機構訂閱連結，則搭配其他專用工具。

## 觸發條件
- 「我要看全文」
- 「有 PDF 嗎？」
- 「這篇有 open access 嗎？」
- 「幫我抓方法或結果段落」
- 提到 PMC、全文、PDF、開放取用

---

## 核心工具

### 1. `get_fulltext`

```python
get_fulltext(source={"kind":"pmcid","value":"PMC7096777"})
get_fulltext(source={"kind":"pmid","value":"12345678"})
get_fulltext(source={"kind":"doi","value":"10.1038/s41586-021-03819-2"})
```

### 參數重點

- `source`: 必填的 discriminated object；`kind` 明確指定 `pmid`、`pmcid` 或 `doi`，`value` 放對應識別碼
- `sections`: 只抓特定段落，例如 `"introduction,methods,results"`
- `include_pdf_links`: 是否回傳 PDF 連結
- `include_figures`: 是否一起帶 figure metadata
- `extended_sources`: 是否擴展到更多來源

---

## 最常用範例

### 1. 直接抓 PMC 全文

```python
get_fulltext(source={"kind":"pmcid","value":"PMC7096777"})
```

### 2. 只看方法與結果

```python
get_fulltext(
    source={"kind":"pmid","value":"12345678"},
    sections="methods,results"
)
```

### 3. 用 DOI 找 OA 版本

```python
get_fulltext(
    source={"kind":"doi","value":"10.1038/s41586-021-03819-2"},
    extended_sources=True
)
```

### 4. 全文連同 figures

```python
get_fulltext(
    source={"kind":"pmcid","value":"PMC7096777"},
    include_figures=True
)
```

---

## 圖表與視覺資料

### 取得文章圖表

```python
get_article_figures(source={"kind":"pmcid","value":"PMC12086443"})
get_article_figures(source={"kind":"pmid","value":"40384072"})
```

適合用在：

- 要單獨抽 figure caption 與 image URL
- 想快速找到流程圖、結果圖、顯微圖
- 需要比全文更結構化的圖像資料

---

## 文字探勘

### 取得 text-mined terms

```python
get_text_mined_terms(source={"kind":"pmcid","value":"PMC7096777"})
get_text_mined_terms(source={"kind":"pmid","value":"12345678"}, semantic_type="CHEMICAL")
```

常用 `semantic_type`:

- `GENE_PROTEIN`
- `DISEASE`
- `CHEMICAL`
- `ORGANISM`
- `GO_TERM`

---

## 沒有 open access 時

### 機構訂閱工作流

```python
list_resolver_presets()
configure_institutional_access(preset="exlibris_sfx", base_url="https://your-library...")
test_institutional_access()
get_institutional_link(pmid="12345678")
```

這一組工具適合：

- 已知機構有訂閱，但文章不是 OA
- 想把 PubMed/DOI 轉成圖書館 resolver 連結

---

## 建議工作流程

### 情境 1：從文章直接拿全文

```python
fetch_article_details(pmids="12345678")
get_fulltext(source={"kind":"pmid","value":"12345678"}, sections="abstract,results")
```

### 情境 2：搜尋後挑代表性文章讀全文

```python
unified_search(
    query="remimazolam ICU sedation",
    limit=10,
    ranking="quality"
)

# 對選中的 PMID 再做全文抓取
get_fulltext(source={"kind":"pmid","value":"12345678"}, extended_sources=True)
```

### 情境 3：先抓全文，再抽圖表與實體

```python
get_fulltext(source={"kind":"pmcid","value":"PMC7096777"}, include_figures=True)
get_article_figures(source={"kind":"pmcid","value":"PMC7096777"})
get_text_mined_terms(source={"kind":"pmcid","value":"PMC7096777"}, semantic_type="CHEMICAL")
```

---

## 工具選擇規則

### 只想拿全文或 PDF

先用 `get_fulltext`

### 想抓圖表

用 `get_article_figures`

### 想抽 gene / disease / chemical

用 `get_text_mined_terms`

### 文章不是 OA，但你有圖書館帳號

用 `configure_institutional_access` + `get_institutional_link`

---

## 最後原則

全文工作流已經收斂成單一公開入口：先用 `get_fulltext`，需要圖表或 text mining 時再補用專用工具。