# SanYu Research Engine

> 106 deterministic medical research tools: stats, survival, bioinformatics, primers. Auditable.

- name: io.github.kfx2026/sanyu-research
- version: 1.1.0
- connection class: R0 (autonomous)
- trust: 97/100
- quality: 77/100 (new)
- owner: not claimed
- descriptor: https://api.protogrid.dev/v1/servers/io.github.kfx2026%2Fsanyu-research
- tools: https://api.protogrid.dev/v1/servers/io.github.kfx2026%2Fsanyu-research/tools

**An agent can connect right now, no human step.** Remote endpoint, no authentication, reachable on the last probe.

## Connection (mcpServers)

```json
{
  "mcpServers": {
    "sanyu-research": {
      "type": "http",
      "url": "https://sanyu.wentangkeji.com/api/mcp"
    }
  }
}
```


## Trust

- hygiene: 100
- liveness: 100
- freshness: 100
- provenance: 90
- drivers: +repository +namespace-matches-repo +website +reachable +uptime-100% +updated-0d-ago
- Derived from observable signals (official registry feed and our own credential-free probes); no code audit performed.

## Quality 77/100 (new)

### protocol: 75 (weight 25)

- warn `protocol.modern`: newest supported version is 2025-06-18; 2026-07-28 not supported, older versions are deprecated until 2027-07-28
- n/a `protocol.stateless`: only applies to 2026-07-28 servers
- pass `protocol.transport`: streamable HTTP
- n/a `protocol.list_ttl`: only applies to 2026-07-28 servers

### authorization: n/a (weight 15)

- n/a `auth.prm`, `auth.as_metadata`, `auth.cimd`: no remote uses OAuth
- n/a `auth.secret_in_url`: no templated URL

### tool hygiene: 79 (weight 30)

- pass `tools.descriptions`: every tool has a description
- pass `tools.description_length`: descriptions are concise
- pass `tools.schemas`: every tool has an object input schema
- pass `tools.annotations`: 146 of 146 tools declare readOnlyHint or destructiveHint
- fail `tools.directory_hints`: no tool declares all four of readOnlyHint, destructiveHint, idempotentHint and openWorldHint; missing: sanyu_list_actions (destructiveHint, idempotentHint, openWorldHint); act_cover_letter (idempotentHint); act_decision_arbitration (idempotentHint); …
- warn `tools.token_cost`: about 18,699 tokens to load every tool
- pass `tools.api_dump`: tools are not a one-to-one API dump

### stability: n/a (weight 15)

- n/a `stability.changes`, `stability.rug_pull`: tool history recorded for 1 of the 7 days needed

### dependencies: n/a (weight 15)

- n/a `deps.known_vulns`, `deps.mcp_sdk_version`, `deps.resolvable`: no npm or PyPI package

- tools: 146, about 18,699 tokens to load them all
- tool set hash: aac54c093d647f73f29e25247b3ca5e8
- tools last changed: 2026-11-07T16:58:00.983Z
- badge: [![protogrid quality](https://protogrid.dev/badge/io.github.kfx2026/sanyu-research.svg)](https://protogrid.dev/servers/io.github.kfx2026/sanyu-research)
- Checks run on what our credential-free, read-only probes observe; tools are never called and no code audit is performed.

### Tool changes

- 2026-11-07 `act_two_way_anova` removed
  - 【双因素方差分析】 用户有两个因素要看主效应与交互作用 返回：主效应与交互 （真实算法引擎执行，非大模型生成）
- 2026-11-07 `act_tox_predict` removed
  - 【毒性预测（hERG/DILI）】 用户要评估化合物心脏/肝毒性风险 返回：毒性风险分级 （真实算法引擎执行，非大模型生成）
- 2026-11-07 `act_survival_analyze` removed
  - 【生存分析（KM/Cox 全流程）】 用户有随访时间与结局事件，要做生存分析、算 HR、出生存曲线 返回：生存曲线、中位生存、HR、log-rank P （真实算法引擎执行，非大模型生成）
- 2026-11-07 `act_stat_center` removed
  - 【统计中心（97 个统计工具统一入口）】 用户要跑的统计检验没被其他专门工具覆盖时用这个：比较差异、找关系、看生存、做诊断、队列预处理、综合证据六类共 97 个工具（Kappa、Bland-Altman、ICC、VIF、PCA、聚类、Friedman、McNemar、游程、符号检验、趋势卡方、Meta 各件等都从这里走） 返回：该统计工具的完整结果（含统计量、P 值、95%CI、解读） （真实算法引擎执行，非大模型生成）
- 2026-11-07 `act_smd_balance` removed
  - 【SMD 均衡性检查】 用户做 PSM/IPTW 后要检查协变量均衡（标准化均数差） 返回：各协变量 SMD 与均衡判断 （真实算法引擎执行，非大模型生成）
- 2026-11-07 `act_sequence_plasmid` removed
  - 【质粒图谱】 用户要画质粒图谱、看载体结构 返回：质粒图谱 （真实算法引擎执行，非大模型生成）
- 2026-11-07 `act_sequence_enzyme` removed
  - 【酶切位点分析】 用户问序列上有哪些酶切位点、能不能切开 返回：酶切位点清单 （真实算法引擎执行，非大模型生成）
- 2026-11-07 `act_scrna_markers` removed
  - 【单细胞 Marker 基因】 用户要找各细胞群的 marker 基因 返回：各群 marker （真实算法引擎执行，非大模型生成）
- 2026-11-07 `act_roc` removed
  - 【ROC 曲线与 AUC】 用户有金标准标签和预测分数，要算 AUC、画 ROC 返回：AUC、最优阈值、敏感度特异度 （真实算法引擎执行，非大模型生成）
- 2026-11-07 `act_psm_match` removed
  - 【倾向性评分匹配 PSM】 用户要做 PSM、倾向评分匹配、1:1 匹配基线 返回：匹配后样本与均衡性 （真实算法引擎执行，非大模型生成）

Full history: https://api.protogrid.dev/v1/servers/io.github.kfx2026%2Fsanyu-research/changes

## Remotes

- https://sanyu.wentangkeji.com/api/mcp (streamable-http, auth none, reachable true, uptime30d 1)

## Packages

_none_

## Tools (146)

- `act_cover_letter`: 投稿信 Cover Letter 用户要写投稿信、给编辑的信 投稿信正文
- `act_decision_arbitration`: 决策引擎裁决 用户要在多个方案/管线之间做取舍、要量化裁决 裁决结果与理由
- `act_deep_review`: 深度审稿（六维） 用户要一份接近真实审稿人的深度意见 深度审稿报告
- `act_design_decision`: 研究设计决策推荐 用户问「这个课题该用什么研究设计」「RCT 还是队列」「样本怎么取」，需要基于 PICO 给出设计类型与理由（决策树 + 评分矩阵，非大模型猜测） 推荐设计类型、框架、推荐依据、分配比例
- `act_design_export`: 导出研究方案书 用户说「把方案导出来」「给我完整的方案书」「生成方案文档」。**导出前应先跑「方案导出前检查」**，有阻断项要如实告知用户，不要直接导出 方案书全文（分章节：题目/PICO/设计/假设/纳排/样本量/伦理/变量字典/干预/随机化/偏倚/预算）
- `act_design_export_check`: 方案导出前检查 用户说「能不能导出了」「帮我检查方案有没有漏洞」「投稿/伦理提交前看看」，或要导出方案书之前必须跑这一步：含 SPIRIT 2025 覆盖度、GCP 审批状态、以及 G3 文档一致性（样本量是否前后一致、主要结局是否写进正文） can_export、SPIRIT 覆盖度与缺失项、G3 一致性问题清单与改法、阻断原因
- `act_design_generate`: 生成研究方案 用户要一份完整研究方案（含样本量、统计计划、伦理要点） 研究方案正文
- `act_design_next_step`: 建议下一步该做什么 用户问「接下来做什么」「下一步填什么」，或刚保存完一步想知道还缺哪几步 下一步的 step 编号、名称、为什么需要它
- `act_design_pico_refine`: PICO 结构化精炼 用户说「帮我精修 PICO」「把这句话做成 PICO」「PICO 怎么拆」，或拿着一句模糊的临床问题要做成可研究的问题；本动作只做结构化，**不编造**用户没说的内容，缺的要素会在结果里标出 PICO 四要素结构化结果 + 提示缺哪些要素
- `act_experiment_plan`: 实验方案生成 用户要做动物/细胞实验方案、实验设计（基础科研） 实验方案
- `act_idea_ignite`: 想法点燃 用户只给一个模糊想法/一个词，要展开成可做研究的方向 扩展研究方向
- `act_lit_digest`: 文献摘要卡生成 用户给一篇文献要生成结构化摘要卡 摘要卡
- `act_literature_compare`: 与已有文献对比 用户想知道自己的发现和已发表文献比新在哪 对比结论
- `act_lit_systematic_review`: 系统评价辅助 用户要做系统评价/Meta 的文献筛选与提取 系统评价框架
- `act_material_mermaid`: 生成流程图（Mermaid） 用户要画流程图/时序图/思维导图（Mermaid） Mermaid 代码
- `act_nsfc_analysis`: 国自然申报分析 用户问国自然/省自然能不能报、报什么口子、命中率 国自然分析
- `act_paper_review`: 论文预审（六智能体） 用户投稿前想先被审一遍、找会被拒的理由 审稿意见与评分
- `act_pdf_local_compare`: 本地文献对比 用户要给一个主题、做站内文献横向对比 对比表
- `act_pdf_pico`: 从文本抽取 PICO 用户贴了一段文献摘要要抽出 PICO 结构化 PICO
- `act_pdf_review_outline`: 文献综述提纲 用户要基于精读文献生成综述提纲 综述提纲
- `act_polish`: 中英文润色 用户要把写好的段落润色、改学术英语、去中式表达 润色后文本
- `act_predict_success`: 录用概率预测 用户问这篇稿子投某刊中稿概率有多大 中稿概率与依据
- `act_pre_review`: 投稿前预审 用户投稿前要先被审一遍、找会被拒的点 预审意见
- `act_proposal_review`: 标书评审 用户贴了标书/方案正文要打分、找问题、提修改意见 五维评分与修改建议
- `act_results_paragraph`: 结果段落生成 用户有统计结果，要自动写成结果段落 结果段落
- `act_revision_track`: 审稿意见回复追踪 用户有审稿意见要逐条回复 逐条回复草稿
- `act_roadmap`: 研究技术路线图 用户要技术路线图、流程图、标书里的研究路线 SVG 路线图
- `act_topic_feasibility`: 选题可行性评估 用户问这个题能不能做、要多少病例、做得完做不完 可行性评分与建议
- `act_topic_gaps`: 研究空白分析 用户问这个领域还有什么没做的、哪里有空白、从哪切入 研究空白清单
- `act_topic_generate`: AI 生成研究选题 用户想找选题、不知道做什么方向、给一个科室/疾病要几个可做的题目 若干候选选题
- `act_topic_lit_review`: 选题文献综述 用户要就一个题目做文献综述、研究现状梳理 综述文本
- `act_topic_novelty`: 选题新颖性检索 用户问这个题目有没有人做过、新不新、会不会重复 新颖性评估与相似研究
- `act_topic_ppt`: 选题汇报 PPT 用户要把选题做成 PPT、汇报材料 PPT 内容
- `act_topic_prominence`: 选题显示度分析 用户想知道某个研究方向热不热、有没有影响力 显示度指标
- `act_topic_research_plan`: 生成研究计划 用户有了题目要一份研究计划/技术路线/研究方案框架 研究计划文本
- `act_top_journal_mining`: 顶刊挖掘 用户问某个领域顶刊在发什么、怎么对标顶刊选题 目标期刊与选题倾向
- `act_write_chapter`: 写单个章节 用户只要写某一段/某一章（摘要、方法、讨论、引言） 该章节正文
- `act_writing_one_click`: 一键生成论文初稿（主线） 用户要写论文、写整篇、一键出初稿、生成正文 初稿任务（后台逐章生成）
- `act_writing_outline`: 生成论文提纲 用户先要提纲/目录/章节结构，再写正文 章节提纲
- `animal_dose_volume`: Animal dose & injection volume / 动物给药量计算 | USE WHEN: compute the dose (mg) and injection volume (mL) for an animal from body weight and mg/kg | ALIASES: 动物给药量, 给药体积, 按体重给药, dose volume, mg/kg, 注射体积, 灌胃体积 | RETURNS: 给药量与体积
- `animal_randomization`: Animal randomization / 动物随机分组 | USE WHEN: randomly assign animals to groups as required by ARRIVE | ALIASES: 动物随机分组, animal randomization, 分组表, 动物分组 | RETURNS: 分组表
- `animal_sample_size`: Animal sample size (3R) / 动物实验样本量 | USE WHEN: how many animals per group are needed (power-based, ARRIVE/3R wording) | ALIASES: 动物样本量, 动物要多少只, 每组几只, animal sample size, 3R, power animals | RETURNS: 每组动物数
- `animal_survival_km`: Animal survival analysis / 动物生存分析 | USE WHEN: Kaplan-Meier and log-rank on animal death/survival times | ALIASES: 动物生存分析, 生存曲线, log-rank, animal survival, KM, 死亡时间 | RETURNS: KM 曲线与 log-rank
- `arrive_checklist`: ARRIVE 2.0 checklist / ARRIVE 2.0 检查 | USE WHEN: check an animal experiment against the ARRIVE 2.0 reporting guideline | ALIASES: ARRIVE, 动物实验规范, 动物伦理报告, animal reporting, ARRIVE 2.0 | RETURNS: ARRIVE 条目核对
- `atac_diff`: ATAC-seq differential accessibility / ATAC 差异可及性 | USE WHEN: differential open regions from ATAC-seq counts | ALIASES: ATAC, 差异可及性, 染色质开放, differential accessibility, peak差异 | RETURNS: 差异 peak
- `bioequivalence_compare`: Bioequivalence (BE) / 生物等效性评价 | USE WHEN: compare test vs reference AUC/Cmax and judge 80-125% bioequivalence with 90% CI | ALIASES: 生物等效性, BE, AUC Cmax, 90%CI, 等效性判定, bioequivalence | RETURNS: 90%CI 与等效性判定
- `calibration_curve`: Calibration curve (prediction model) / 校准曲线 | USE WHEN: assess calibration of a risk model and get the curve plus Brier score | ALIASES: 校准曲线, 校准度, Brier, calibration, 预测模型校准, 可靠性曲线 | RETURNS: 校准曲线与 Brier 分数
- `cell_assay_calc`: Cell assay calculator / 细胞实验通用计算 | USE WHEN: CCK8 / wound healing / Transwell / colony formation calculations (pick tool_id: cck8, wound-healing, transwell, colony) | ALIASES: CCK8, 划痕实验, Transwell, 克隆形成, 细胞增殖, 迁移实验, cell viability, wound healing | RETURNS: 计算结果
- `chip_peak_calling`: ChIP-seq peak calling / ChIP-seq Peak calling | USE WHEN: call peaks from ChIP-seq reads | ALIASES: ChIP-seq, peak calling, 峰识别, 转录因子结合, ChIP | RETURNS: peak 清单
- `citation_format`: Citation formatting / 引文格式化 | USE WHEN: reformat references to Vancouver / APA / GB-T 7714 | ALIASES: 参考文献格式, 引文格式, Vancouver, APA, GB/T 7714, citation, 文献格式 | RETURNS: 格式化后的引文
- `citation_verify`: Verify citation authenticity / 引文真实性核验 | USE WHEN: check whether references are fabricated (existence check against the local corpus) | ALIASES: 核验参考文献, 引用是真的吗, 假文献, citation verify, 参考文献真实性, hallucinated reference | RETURNS: 逐条核验结果
- `classification_metrics`: Classification metrics / 分类性能报告 | USE WHEN: full diagnostic performance set: accuracy, sensitivity, specificity, PPV, NPV, F1 | ALIASES: 分类性能, 准确率, 敏感度, 特异度, PPV, NPV, confusion matrix, 诊断指标 | RETURNS: 分类指标全套
- `clinical_basic_bridge`: Clinical ↔ basic translation bridge / 临床↔基础转化配对 | USE WHEN: from a clinical finding propose mechanistic hypotheses and experiments (or the reverse) | ALIASES: 转化研究, 临床到基础, 机制假说, bench to bedside, Bridge, 实验方案建议 | RETURNS: 转化研究假说与实验方案
- `cloning_design`: Molecular cloning design / 克隆方案设计 | USE WHEN: design a plasmid construct: homology arms, primers, assembly strategy | ALIASES: 分子克隆, 构建质粒, 同源臂, cloning, Gibson, 酶切连接, 引物设计克隆 | RETURNS: 克隆方案与引物
- `consort_flowchart`: CONSORT flow diagram / CONSORT 流程图 | USE WHEN: draw a CONSORT participant flow diagram from screening/randomization/loss counts | ALIASES: CONSORT流程图, 流程图, 入组流程, flow diagram, 筛选失访 | RETURNS: CONSORT 流程图
- `crf_sdtm_validate`: CRF / SDTM validation / CRF 数据校验（SDTM） | USE WHEN: check whether CRF data meets CDISC SDTM conventions before export | ALIASES: CRF校验, SDTM, CDISC, 数据合规, 导出前检查, CRF validate, data standards | RETURNS: 校验结果
- `dataframe_parse`: Parse pasted data / infer column types / 数据解析与列类型识别 | USE WHEN: the user pasted a CSV/table blob: get shape, column types, missingness before analysis | ALIASES: 解析数据, 列类型, 粘贴表格, CSV解析, parse data, column types, 数据形状 | RETURNS: 行列数、列类型、缺失情况
- `dataset_columns`: List data columns / 读取数据列名 | USE WHEN: list the variables in an uploaded data file (do this before Table 1 / modeling so you use real column names) | ALIASES: 列名, 有哪些变量, 字段名, columns, variables, 变量清单, 表头 | RETURNS: 列名与类型
- `dataset_profile`: Dataset profile / 数据快速画像 | USE WHEN: first look at a data file: distributions, missingness, outliers, data quality overview | ALIASES: 数据长什么样, 描述统计, 缺失情况, 数据质量, data profile, descriptive, summary stats, EDA | RETURNS: 描述统计与质量概览
- `delong_auc_compare`: DeLong test (compare two AUCs) / 两条 ROC 的 AUC 比较（DeLong） | USE WHEN: compare two ROC AUCs from the same subjects and test whether the difference is significant | ALIASES: DeLong, 两个AUC比较, 哪个指标更好, AUC差异, compare AUC, 曲线下面积比较 | RETURNS: 两 AUC 及差值 P 值
- `design_gate_rules`: Design gate rules / 方案设计闸门规则 | USE WHEN: explain why a design step is blocked, or what the platform requires for each step (hard gates G1/G2/G3) | ALIASES: 方案设计闸门, 为什么不让过, 硬性要求, gate rules, validation rules, why blocked | RETURNS: 每步是否有硬闸门、三道跨步闸门 G1/G2/G3 的原文规则
- `design_step_context`: Design step context / 取某一步的上下文 | USE WHEN: load what the user already answered for a design step (PICO / design type / population) before drafting the next step | ALIASES: 方案设计上下文, 上一步内容, 已经填过的信息, design context, previous step, project context | RETURNS: 该步骤可用的前序上下文（pico / design_type / population / topic）
- `differential_expression`: Differential expression (limma-style) / 差异表达分析 | USE WHEN: differential genes from an expression matrix (with volcano plot input) | ALIASES: 差异表达, 差异基因, DEG, differential expression, 火山图, 表达矩阵, limma | RETURNS: 差异基因清单
- `dna_translate`: DNA → protein translation / 序列翻译 | USE WHEN: translate a nucleotide sequence into amino acids in all three frames | ALIASES: 序列翻译, 核酸转蛋白, 翻译, translate, DNA to protein, 氨基酸序列, ORF翻译 | RETURNS: 氨基酸序列
- `drug_interaction_check`: Drug-drug interaction check / 药物相互作用检查 | USE WHEN: can these two drugs be used together? interaction severity and mechanism | ALIASES: 药物相互作用, 能不能一起用, 配伍禁忌, DDI, drug interaction, 相互作用 | RETURNS: 相互作用等级与机制
- `dunn_posthoc`: Dunn post-hoc test / 多组非参数两两比较（Dunn） | USE WHEN: pairwise comparisons after Kruskal-Wallis (nonparametric, multiple groups) | ALIASES: Dunn, 非参数两两比较, Kruskal-Wallis后续, post hoc, 多重比较 | RETURNS: 两两比较 P 值
- `egger_bias_test`: Egger's test (publication bias) / Meta 发表偏倚（Egger） | USE WHEN: test publication bias in a meta-analysis and get funnel-plot inputs | ALIASES: Egger, 发表偏倚, 漏斗图, publication bias, meta回归, funnel plot | RETURNS: Egger 检验 P 与截距
- `evidence_followup`: Evidence follow-up Q&A / 证据追问（多轮） | USE WHEN: multi-turn follow-up on an evidence card (causal judgement) | ALIASES: 证据追问, 继续追问, 证据卡, evidence followup, 因果判断 | RETURNS: 追问答复
- `evidence_intake`: Structured evidence intake / 文献证据分级录入 | USE WHEN: decompose a paper into a structured evidence card by evidence level (Decision OS) | ALIASES: 证据分级, 证据卡, 文献拆解, evidence level, GRADE, 证据录入 | RETURNS: 结构化证据卡
- `evidence_report`: Evidence report narrative / 证据报告叙述 | USE WHEN: turn evidence cards into a readable report | ALIASES: 证据报告, 证据综述, evidence report, 证据叙述 | RETURNS: 证据报告正文
- `fibrosis_staging`: Liver fibrosis staging / 纤维化分期 | USE WHEN: map fibrosis scores to Ishak / METAVIR stage | ALIASES: 纤维化分期, Ishak, METAVIR, 肝纤维化, fibrosis stage, 病理分期 | RETURNS: 分期结论与解读
- `figure_legend`: Figure legend writer / 图注生成 | USE WHEN: write a figure legend for a given chart type | ALIASES: 图注, Figure legend, 图片说明, 图例, 写图注 | RETURNS: 图注文本
- `flow_cluster`: Flow cytometry clustering / 流式细胞聚类 | USE WHEN: unsupervised clustering of flow cytometry data into cell populations | ALIASES: 流式聚类, 无监督分群, flow clustering, 细胞群, FlowSOM | RETURNS: 细胞群与比例
- `flow_gate`: Flow cytometry gating / 流式门控分析 | USE WHEN: gating / population percentages from flow cytometry data | ALIASES: 流式门控, 圈门, gating, 阳性比例, flow cytometry | RETURNS: 门控比例
- `forest_plot`: Forest plot / 森林图 | USE WHEN: forest plot from HR/OR/RR with confidence intervals | ALIASES: 森林图, forest plot, HR OR RR, 置信区间图, meta森林图 | RETURNS: 森林图（矢量）
- `gene_info_lookup`: Gene information lookup / 基因信息检索 | USE WHEN: what a gene does: full name, aliases, function, location | ALIASES: 基因信息, 这个基因是干什么的, gene function, 别名, gene info, 基因全称 | RETURNS: 基因信息
- `gene_pathway_disease`: Gene → pathway → disease network / 基因-通路-疾病网络 | USE WHEN: from a gene list, find pathway / disease / drug associations and build a mechanism network | ALIASES: 基因通路, 机制网络, gene pathway, 疾病关联, 药物靶点, network, 通路疾病 | RETURNS: 通路/疾病/药物关联
- `geo_dataset_pipeline`: GEO dataset pipeline / GEO 数据全流程 | USE WHEN: end-to-end on a GEO accession: DEG → enrichment → immune infiltration | ALIASES: GEO, GSE, 数据集分析, GEO编号, geo dataset, public data reanalysis | RETURNS: 差异基因、富集结果
- `gleason_grade_group`: Gleason grade group / Gleason 分级分组 | USE WHEN: Gleason score → Grade Group and risk stratum (prostate cancer) | ALIASES: Gleason, 前列腺癌分级, Grade Group, 格里森评分, 前列腺病理 | RETURNS: Grade Group 与风险分层
- `go_kegg_enrichment`: GO / KEGG enrichment / 通路富集（GO/KEGG） | USE WHEN: pathway enrichment for a gene list (GO biological process, KEGG) | ALIASES: 富集分析, GO, KEGG, 通路富集, enrichment, 通路, 功能注释 | RETURNS: 富集通路与 P 值
- `ic50_fit`: IC50 curve fitting / IC50 曲线拟合 | USE WHEN: fit a dose-response curve and report IC50 with 95% CI | ALIASES: IC50, 半数抑制浓度, 剂量效应曲线, dose response, 抑制率拟合, GI50 | RETURNS: IC50 值与 95%CI
- `ihc_hscore`: IHC scoring (H-score / Allred) / IHC 免疫组化评分 | USE WHEN: immunohistochemistry scoring from staining intensity percentages (H-score, Allred, positive rate) | ALIASES: 免疫组化评分, H-score, Allred, IHC, 阳性率, 染色评分, ER PR HER2 | RETURNS: H-score / Allred / 阳性率
- `iptw_weighting`: IPTW inverse probability weighting / IPTW 逆概率加权 | USE WHEN: propensity-score weighting (ATE/ATT) for observational comparisons | ALIASES: IPTW, 逆概率加权, 倾向评分加权, propensity weighting, 加权后效应, ATE | RETURNS: 权重与加权后效应
- `journal_recommend`: Journal recommendation / 智能选刊 V3 | USE WHEN: recommend target journals from title/abstract, in reach / match / safe tiers | ALIASES: 选刊, 投哪个期刊, 期刊推荐, journal, submission target, 冲稳保, 投稿建议 | RETURNS: 候选期刊与三档策略
- `kaplan_meier`: Kaplan-Meier curve / Kaplan-Meier 曲线 | USE WHEN: KM curve and median survival only (no multivariable model) | ALIASES: KM曲线, Kaplan-Meier, 中位生存, 生存曲线, 生存率, log-rank | RETURNS: KM 曲线与中位生存
- `linear_mixed_model`: Linear mixed model (repeated measures) / 线性混合模型（纵向/重复测量，公式模式） | USE WHEN: same subject measured repeatedly (pre/post, multiple follow-ups): compare trajectories over time, group×time interaction; modern replacement for repeated-measures ANOVA (handles missing, no sphericity assumption) | ALIASES: 混合模型, 重复测量, 纵向数据, LMM, mixed model, 交互作用, 多次随访, 每人测多次, repeated measures | RETURNS: 固定效应系数/SE/Wald z/P/95%CI（按真实变量名，不是 X1/X2）、各项整体检验 omnibus、交互项检验、primary_test、ICC、方差成分、n_subjects/n_obs、组间例数、缺失与收敛告警
- `literature_search_pmc`: Literature search (1.53M PMC full texts) / 文献检索（站内 153 万篇 PMC） | USE WHEN: search the in-house PMC corpus for literature / evidence / state of the art | ALIASES: 找文献, 文献检索, 检索文献, literature search, PMC, 证据, 研究现状, PubMed替代 | RETURNS: 文献列表（含 PMCID）
- `metabolite_heatmap`: Metabolite heatmap / 代谢组热图 | USE WHEN: clustered heatmap of metabolites | ALIASES: 代谢热图, 聚类热图, heatmap, metabolite heatmap | RETURNS: 聚类热图
- `metabolite_pathway`: Metabolite pathway enrichment / 代谢物通路富集 | USE WHEN: pathway enrichment for differential metabolites | ALIASES: 代谢物通路, 代谢富集, metabolite pathway, KEGG代谢, 差异代谢物 | RETURNS: 代谢通路富集
- `metabolite_pca`: Metabolite PCA / 代谢组 PCA | USE WHEN: sample separation / outliers in metabolomics (unsupervised) | ALIASES: 代谢组PCA, PCA, 样本分组趋势, 离群, unsupervised, 得分图 | RETURNS: PCA 得分与解释度
- `metabolite_plsda`: Metabolite PLS-DA / 代谢组 PLS-DA | USE WHEN: supervised discrimination and VIP for differential metabolites | ALIASES: PLS-DA, 有监督判别, VIP, 代谢组判别, 判别分析 | RETURNS: PLS-DA 模型与 VIP
- `metabolite_volcano`: Metabolite volcano plot / 代谢组火山图 | USE WHEN: volcano plot of differential metabolites | ALIASES: 代谢火山图, volcano, 差异代谢物火山图 | RETURNS: 火山图
- `mif_phenotype`: Multiplex IF phenotyping / 多重免疫荧光表型 | USE WHEN: assign cell phenotypes and proportions from multiplex immunofluorescence markers | ALIASES: 多重免疫荧光, mIF, 细胞表型, phenotype, marker组合, 亚群比例 | RETURNS: 细胞亚群表型与比例
- `multi_omics_integrate`: Multi-omics integration / 多组学整合 | USE WHEN: integrate mRNA / protein / metabolite layers and find cross-layer associations | ALIASES: 多组学, 整合分析, multi-omics, 转录组蛋白组代谢组, cross-omics | RETURNS: 多组学关联结果
- `nomogram`: Nomogram / 列线图 Nomogram | USE WHEN: turn a logistic or Cox model into a nomogram figure for clinical use | ALIASES: 列线图, Nomogram, 评分图, nomogram, 风险评分图 | RETURNS: 列线图 SVG
- `nuclei_count`: Nuclei segmentation & counting / 细胞核分割与计数 | USE WHEN: cell density / positive rate from a tissue section (nuclei segmentation) | ALIASES: 细胞核分割, 细胞计数, 密度, nuclei count, segmentation, 阳性细胞率 | RETURNS: 分割统计结果
- `orf_find`: ORF prediction / ORF 预测 | USE WHEN: find open reading frames in a nucleotide sequence | ALIASES: ORF, 开放阅读框, 找编码区, open reading frame, 翻译起始 | RETURNS: ORF 列表与位置
- `organ_coefficient`: Organ coefficient / 脏器系数 | USE WHEN: organ weight / body weight ratio (脏器系数) | ALIASES: 脏器系数, 器官系数, organ coefficient, organ index, 脏器重量比 | RETURNS: 脏器系数
- `pcr_primer_design`: PCR primer design / PCR 引物设计 | USE WHEN: design PCR primer pairs from a DNA sequence or gene (Tm, GC, product size) | ALIASES: 引物设计, PCR引物, primer, 设计引物, 扩增引物, primer pair | RETURNS: 候选引物对与 Tm/GC
- `permutation_test`: Permutation test / 置换检验 | USE WHEN: exact/permutation test when t-test assumptions fail or n is tiny | ALIASES: 置换检验, 随机化检验, permutation, 精确检验, 小样本非参数, resampling | RETURNS: 置换 P 值
- `pico_refine`: PICO refinement / PICO 精炼 | USE WHEN: turn a vague clinical question into a structured PICO (Population / Intervention / Comparator / Outcome) | ALIASES: PICO, 把问题拆开, 研究问题怎么提, refine question, 结构化问题, PICO拆解 | RETURNS: 结构化 PICO
- `pk_nca`: Non-compartmental PK (NCA) / NCA 非房室药代分析 | USE WHEN: non-compartmental analysis of concentration-time data: AUC, Cmax, Tmax, t1/2, CL, Vd | ALIASES: NCA, 非房室分析, 药代参数, AUC Cmax T1/2, PK parameters, 血药浓度 | RETURNS: AUC/Cmax/Tmax/T1/2/CL/Vd
- `pk_pd_assess`: PK/PD assessment / PK/PD 评估 | USE WHEN: pharmacokinetic/pharmacodynamic assessment and dose projection | ALIASES: PK/PD评估, 药代药效, 剂量推算, pkpd assess, 暴露量 | RETURNS: PK/PD 评估结果
- `pkpd_simulate`: PK/PD simulation / PK/PD 模拟 | USE WHEN: simulate concentration-effect time course for a dosing regimen | ALIASES: PK/PD模拟, 给药方案模拟, 浓度时间曲线, simulation, 剂量方案 | RETURNS: 模拟曲线与参数
- `plasmid_map`: Plasmid map / 质粒图谱 | USE WHEN: draw/describe a plasmid map from a sequence (features, orientation) | ALIASES: 质粒图谱, 载体结构, plasmid map, vector, 质粒构建图 | RETURNS: 质粒图谱
- `ppi_network`: Protein-protein interaction network / 蛋白互作网络 | USE WHEN: PPI network from a protein list, hub identification (STRING) | ALIASES: 蛋白互作, PPI, STRING, hub基因, 互作网络, network | RETURNS: 互作网络与 hub 蛋白
- `primer_tm`: Primer Tm & dimers / 引物 Tm 计算 | USE WHEN: Tm, GC% and self/cross dimer check for given primer sequences | ALIASES: Tm, 引物Tm, 退火温度, GC含量, 二聚体, primer dimer, melting temperature | RETURNS: Tm、GC、二聚体
- `prisma_flowchart`: PRISMA flow diagram / PRISMA 流程图 | USE WHEN: PRISMA literature screening flow for systematic reviews / meta-analyses | ALIASES: PRISMA, 文献筛选流程, 系统评价流程图, flow diagram, 检索流程图 | RETURNS: PRISMA 流程图
- `propensity_score_matching`: Propensity score matching (PSM) / 倾向性评分匹配 PSM | USE WHEN: match treated and control subjects on baseline covariates (1:1 nearest neighbour within caliper), then report SMD balance | ALIASES: PSM, 倾向性评分, 倾向评分匹配, propensity score, 1:1匹配, 基线可比, 混杂控制, 匹配后均衡 | RETURNS: 匹配后样本与均衡性
- `protein_digest`: In-silico protein digestion / 蛋白酶解模拟 | USE WHEN: choose protease and list peptides for mass-spec sample prep | ALIASES: 蛋白酶解, 质谱前处理, 酶解肽段, digest, trypsin, 肽段清单 | RETURNS: 肽段清单
- `protein_enrichment`: Protein enrichment / 蛋白富集分析 | USE WHEN: GO/KEGG enrichment for a protein list | ALIASES: 蛋白富集, protein enrichment, 蛋白GO, 蛋白通路 | RETURNS: 富集通路
- `protein_physchem`: Protein physicochemical properties / 蛋白理化性质 | USE WHEN: molecular weight, pI, GRAVY, instability index etc. from a protein sequence | ALIASES: 蛋白理化性质, 分子量, 等电点, pI, GRAVY, 疏水性, MW, protein properties | RETURNS: MW/pI/GRAVY 等
- `protein_ptm`: Post-translational modification prediction / 翻译后修饰分析 | USE WHEN: predict phosphorylation / glycosylation and other PTM sites | ALIASES: 翻译后修饰, 磷酸化位点, 糖基化, PTM, phosphorylation, modification sites | RETURNS: 修饰位点预测
- `psi_stability`: PSI model stability / 模型稳定性 PSI | USE WHEN: population stability index between expected and observed score distributions | ALIASES: PSI, 模型稳定性, 群体稳定性, population stability, 漂移 | RETURNS: PSI 值与稳定性判断
- `pubmed_search`: PubMed search / PubMed 检索 | USE WHEN: search PubMed for the latest literature (live NCBI E-utilities) | ALIASES: PubMed, 查pubmed, 最新文献, pubmed检索, NCBI, 最新研究 | RETURNS: PubMed 文献列表
- `qpcr_bayesian_design`: qPCR Bayesian design / 贝叶斯自适应设计（qPCR） | USE WHEN: design a qPCR experiment / digital-twin PCR simulation for a target | ALIASES: qPCR设计, 贝叶斯自适应, digital PCR, 实验设计, target, 扩增效率 | RETURNS: 实验设计方案
- `qpcr_ddct`: qPCR 2^-ddCt relative expression / qPCR 相对表达量（2^-ΔΔCt） | USE WHEN: compute dCt, ddCt and 2^-ddCt fold change from target and reference Ct values, with a group test (calibrated on the mean control dCt) | ALIASES: qPCR, 2^-ddCt, 相对表达量, 倍数, dCt, ddCt, fold change, 内参, Ct值, 基因表达 | RETURNS: fc（2^-ΔΔCt 相对倍数，已按对照组 ΔCt 均值校准）、fold_values、p_value、stat_method（welch_ttest_on_dct / student_ttest_on_dct …
- `randomization_allocate`: Randomization / allocation / 随机化分组 | USE WHEN: produce a reproducible randomization list: simple, block, stratified, or minimization, with a fixed seed | ALIASES: 随机分组, 随机数字表, 区组随机, 分层随机, 最小化法, randomization, random allocation, 分组名单, seed | RETURNS: 分组结果表
- `renal_dose_adjust`: Renal function & dose adjustment / 肾功能剂量调整 | USE WHEN: the user gives age / sex / weight / serum creatinine and asks for creatinine clearance, eGFR, CKD stage, or how to adjust a drug dose in renal impairment (vancomycin, meropenem, metformin, anticoagulants…) | ALIASES: 肾功能剂量, 肌酐清除率, CrCl, eGFR, 肾小球滤过率, 减量, 剂量调整, 肾功能不全, 万古霉素怎么减量, 按肌酐算剂量 | RETURNS: CrCl（Cockcroft-Gault）+ eGFR（CKD-EPI 2021）+ CKD 分期 + 该药的肾剂量分档（含依据与核对提示）
- `reporting_checklist`: Reporting guideline checklist / 报告规范清单核对 | USE WHEN: check a manuscript against CONSORT / STROBE / PRISMA / TRIPOD / ARRIVE / STARD | ALIASES: 报告规范, CONSORT, STROBE, PRISMA, TRIPOD, STARD, checklist, 投稿前检查, 规范核对 | RETURNS: 逐条核对结果
- `restriction_sites`: Restriction sites / 酶切位点分析 | USE WHEN: find restriction enzyme sites in a DNA sequence (can it be cut, which enzyme) | ALIASES: 酶切位点, 限制性内切酶, restriction enzyme, 能不能切开, cloning sites | RETURNS: 酶切位点清单
- `risk_ratio_or`: Risk ratio / odds ratio / 相对危险度 RR | USE WHEN: RR, OR and 95% CI from a 2×2 table of counts | ALIASES: 相对危险度, RR, OR, 比值比, 四格表, risk ratio, odds ratio, 95%CI | RETURNS: RR、OR、95%CI
- `rnaseq_deg`: RNA-seq differential analysis / RNA-seq 差异分析 | USE WHEN: differential genes from RNA-seq count matrix (negative binomial) | ALIASES: RNA-seq, 转录组差异, 差异基因, DESeq, count matrix, RNA测序 | RETURNS: 差异基因清单
- `roc_auc`: ROC curve & AUC / ROC 曲线与 AUC | USE WHEN: the user has a binary gold standard and predicted scores/probabilities and wants AUC, ROC curve, discrimination, or the best cut-off | ALIASES: ROC, AUC, 曲线下面积, 诊断效能, 最佳阈值, cutoff, discrimination, 敏感度特异度, ROC曲线 | RETURNS: AUC、最优阈值、敏感度特异度
- `sample_size_calculator`: Sample size & power / 样本量计算（设计口径） | USE WHEN: calculate how many subjects are needed (two means / two proportions / correlation / survival), or solve for power | ALIASES: 样本量, 要做多少例, 需要多少病人, sample size, power calculation, 把握度, 最少例数, n per group | RETURNS: 每组例数与合计
- `sanyu_list_actions`: List every tool on this server as a compact catalogue (name / when-to-use / returns / required params / cost). 列出三域医研全部动作摘要。仅在不确定该用哪个工具时调用；已知工具名可直接调用对应工具。
- `sas_r_code`: Reproducible SAS/R/Python code / 生成可复现 SAS/R 代码 | USE WHEN: generate the SAS / R / Python code that reproduces a given analysis | ALIASES: SAS代码, R代码, 复现代码, reproducible code, Python代码, 统计代码 | RETURNS: 推荐统计方法与代码
- `scrna_annotate`: Single-cell type annotation / 单细胞细胞类型注释 | USE WHEN: automatically annotate cell types in single-cell clusters | ALIASES: 细胞类型注释, annotation, 单细胞注释, cell type, 自动注释 | RETURNS: 细胞类型注释
- `scrna_cluster`: Single-cell clustering / 单细胞分析 | USE WHEN: cluster and embed single-cell data (UMAP) | ALIASES: 单细胞, scRNA, 聚类降维, UMAP, Seurat, single cell | RETURNS: 聚类结果与 UMAP
- `scrna_markers`: Single-cell marker genes / 单细胞 Marker 基因 | USE WHEN: find marker genes per cluster | ALIASES: marker基因, 各群marker, markers, 差异标记, cluster markers | RETURNS: 各群 marker
- `shap_explain`: SHAP single-case explanation / SHAP 单样本解释 | USE WHEN: explain why one patient was predicted high risk (feature contributions) | ALIASES: SHAP, 解释预测, 为什么高风险, 特征贡献, explain prediction, 可解释性 | RETURNS: SHAP 贡献图
- `smd_balance`: SMD balance check / SMD 均衡性检查 | USE WHEN: standardized mean differences before/after PSM or IPTW (covariate balance) | ALIASES: SMD, 标准化均数差, 均衡性, balance, 匹配后均衡, 协变量平衡, love plot | RETURNS: 各协变量 SMD 与均衡判断
- `species_dose_convert`: Species dose conversion / 种属剂量换算 | USE WHEN: convert an animal dose to a human equivalent dose (mg/kg ↔ mg/m², FDA guidance) | ALIASES: 种属换算, 动物剂量换人, 剂量换算, species conversion, HED, mg/m2, 体表面积法 | RETURNS: 换算后剂量与依据
- `stats_toolbox`: Statistics toolbox (97 tests) / 统计中心（97 个统计工具统一入口） | USE WHEN: any statistical test not covered by a dedicated tool: Kappa, ICC, Bland-Altman, VIF, PCA, cluster, Friedman, McNemar, runs test, sign test, trend chi-square, meta-analysis pieces — pass tool_id | ALIASES: 统计检验, Kappa, ICC, Bland-Altman, VIF, PCA, 聚类, Friedman, McNemar, 游程 | RETURNS: 该统计工具的完整结果（含统计量、P 值、95%CI、解读）
- `study_design_advisor`: Study design advisor / 研究设计向导 | USE WHEN: the user has a research question but does not know which study design to use (RCT / cohort / case-control / cross-sectional / diagnostic accuracy) | ALIASES: 研究设计选哪个, 设计向导, 用什么研究设计, study design, which design, 研究类型, 队列还是病例对照 | RETURNS: 推荐研究设计与理由
- `survival_cox`: Survival analysis (KM + Cox) / 生存分析（KM/Cox 全流程） | USE WHEN: follow-up time and event data: survival curves, median survival, hazard ratios, log-rank, multivariable Cox | ALIASES: 生存分析, Cox回归, HR, 风险比, log-rank, 中位生存, 随访, 生存率, hazard ratio | RETURNS: 生存曲线、中位生存、HR、log-rank P
- `table_one_baseline`: Table 1 (baseline characteristics) / 生成 Table 1（基线特征表） | USE WHEN: build the paper's first table: baseline characteristics by group with P values | ALIASES: Table 1, 基线表, 基线特征表, 人口学表, 第一张表, baseline table, characteristics table, 组间比较表 | RETURNS: Table 1 表格 + 可选段落
- `table_one_subgroup`: Subgroup Table 1 / 分组 Table 1（亚组表） | USE WHEN: baseline characteristics split by a subgroup variable (e.g. by sex, by centre) | ALIASES: 亚组基线表, 分层基线表, subgroup table, stratified baseline, 按某变量分层 | RETURNS: 亚组表
- `text_overlap_check`: Text overlap self-check / 文字重合度自查 | USE WHEN: compare a passage with the user's own previous papers (self-plagiarism check) | ALIASES: 查重, 文字重合度, 重复率, overlap, self plagiarism, 相似文献 | RETURNS: 重合度与相似文献
- `toxicity_predict`: Toxicity prediction (hERG / DILI) / 毒性预测（hERG/DILI） | USE WHEN: cardiac (hERG) and liver (DILI) toxicity risk from a SMILES string | ALIASES: 毒性预测, hERG, DILI, 心脏毒性, 肝毒性, toxicity, SMILES | RETURNS: 毒性风险分级
- `toxicology_assess`: Toxicology assessment / 毒理评估 | USE WHEN: nonclinical toxicology risk: hERG / DILI / genotoxicity, overall risk level | ALIASES: 毒理评估, 毒性风险, 遗传毒, toxicology, nonclinical safety, 安全评价 | RETURNS: 毒性风险分级
- `tukey_hsd`: Tukey HSD post-hoc / 多组两两比较（Tukey HSD） | USE WHEN: pairwise comparisons after one-way ANOVA with family-wise error control | ALIASES: Tukey, HSD, ANOVA后续, 两两比较, post hoc, 多重比较 | RETURNS: 两两比较 P 值
- `tumor_growth_inhibition`: Tumour growth inhibition (TGI) / 抑瘤率 TGI | USE WHEN: compute TGI (%) from control vs treated tumour volumes, with a group comparison | ALIASES: 抑瘤率, TGI, 肿瘤抑制率, tumour growth inhibition, 抑瘤实验, 药效评价 | RETURNS: TGI 与统计检验
- `tumor_microenvironment`: Tumour microenvironment / 肿瘤微环境分析 | USE WHEN: assess tumour immune microenvironment from TILs / CD8 / PD-L1 data (typing + prognosis hint) | ALIASES: 肿瘤微环境, TME, 免疫浸润, TIL, PD-L1, CD8, immune microenvironment | RETURNS: 微环境分型与预后提示
- `tumor_volume_curve`: Tumour volume & growth curve / 肿瘤体积计算 | USE WHEN: tumour volume from caliper length/width and the growth curve over days | ALIASES: 肿瘤体积, 长短径算体积, 生长曲线, tumor volume, caliper, 生长抑制曲线 | RETURNS: 体积与生长曲线
- `two_way_anova`: Two-way ANOVA / 双因素方差分析 | USE WHEN: two factors: main effects and interaction | ALIASES: 双因素方差分析, two-way ANOVA, 交互作用, 主效应, 两个因素 | RETURNS: 主效应与交互