Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@KnowS MCP Server search for evidence on type 2 diabetes treatments"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
KnowS 医学证据检索与分析平台的 MCP (Model Context Protocol) Server。
本包发布在 GitHub Packages，需要先配置 npm registry。在 ~/.npmrc 中添加：
//npm.pkg.github.com/:_authToken=YOUR_GITHUB_TOKEN
@PancrePal-xiaoyibao:registry=https://npm.pkg.github.com
YOUR_GITHUB_TOKEN需要有read:packages权限。
变量 | 必填 | 说明 |
| 是 | KnowS API 密钥 |
| 否 | API 地址，默认 |
在 ~/.claude/settings.json 或项目 .claude/settings.json 中添加：
{
"mcpServers": {
"knows": {
"command": "npx",
"args": ["@PancrePal-xiaoyibao/knows-mcp-server"],
"env": {
"KNOWS_API_KEY": "your_api_key",
"KNOWS_API_HOST": "https://api.nullht.com"
}
}
}
}在 ~/Library/Application Support/Claude/claude_desktop_config.json（macOS）中添加：
{
"mcpServers": {
"knows": {
"command": "npx",
"args": ["@PancrePal-xiaoyibao/knows-mcp-server"],
"env": {
"KNOWS_API_KEY": "your_api_key",
"KNOWS_API_HOST": "https://api.nullht.com"
}
}
}
}# 通过 npx
KNOWS_API_KEY=your_key npx @PancrePal-xiaoyibao/knows-mcp-server
# 或全局安装后运行
npm install -g @PancrePal-xiaoyibao/knows-mcp-server
KNOWS_API_KEY=your_key knows-mcp-serverRelated MCP server: Agent Care MCP
# 安装依赖
npm install
# 使用 bun 开发（热重载）
KNOWS_API_KEY=your_key bun run dev
# 构建
npm run build
# 运行构建产物
KNOWS_API_KEY=your_key npm start工具 | 说明 |
| AI 检索，根据问题返回相关医学证据列表 |
| 单篇证据 AI 概要总结 |
| 获取所有证据的 AI 总结 |
| 获取证据被引用的原文内容 |
| 基于问题和证据生成场景总结 |
| 场景总结（流式收集） |
| 证据自动化标签提取 |
| 英文文献详情 |
| 中文文献详情 |
| 指南详情 |
| 会议详情 |
| 历史提问列表 |
| 文献解读历史列表 |
| 通过 PDF 创建新证据 |
项目附带了推荐的系统提示词 SYSTEM_PROMPT.md，用于指导 AI 助手基于 KnowS 工具进行结构化的医学文献调研。
# 更新 package.json 中的 version 后
git tag v0.x.x
git push origin v0.x.x推送 v* tag 后，GitHub Actions 会自动运行测试并发布到 GitHub Packages。
AI 检索：用户提问，返回与该问题相关的医学证据列表
Inputs: query (required), data_scope (required).
获取所有证据的单篇 AI 总结（收集完整的流式响应后返回）
Inputs: no named parameters.
基于问题和检索出来的证据，生成场景总结（非流式）
Inputs: question_id (required), answer_type (required).
基于问题和检索出来的证据，生成场景总结（收集完整的流式响应后返回）
Inputs: question_id (required), answer_type (required).
对证据进行自动化标签提取（如研究类型、疾病领域、样本量等）
Inputs: tagging_type (required), content, evidence_id.
通过上传 PDF 文件创建新的证据
Inputs: pdf_base64 (required).
返回单篇证据被引用的原文部分（高亮内容）
Inputs: evidence_id (required).
对单篇证据进行 AI 概要总结
Inputs: evidence_id (required).
获取医学指南的详细信息
Inputs: evidence_id (required), translate_to_chinese.
获取医学会议记录的详细信息
Inputs: evidence_id (required), translate_to_chinese.
获取中文文献的详细信息
Inputs: evidence_id (required).
获取英文文献的详细信息（标题、摘要、影响因子、分区等）
Inputs: evidence_id (required), translate_to_chinese.
获取用户的单篇文献 AI 解读历史记录，支持时间范围和分页
Inputs: from_time, to_time, page, page_size.
获取用户的历史提问列表，支持时间范围和分页
Inputs: from_time, to_time, page, page_size.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
Scored across 14 tools
Most tools have distinct purposes (e.g., ai_search vs. answer vs. evidence_summary). However, answer and answer_stream are clearly variants of the same operation, and all_evidence_summary and evidence_summary overlap in scope, causing slight ambiguity.
Naming patterns are mixed: some tools use verb_noun (get_guide, list_questions) while others use noun_verb or noun_noun (evidence_highlight, ai_search). The presence of answer and answer_stream as single verbs breaks the pattern, reducing consistency.
With 14 tools covering search, summarization, evidence management, and history, the count feels well-scoped for a medical knowledge server. Each tool serves a clear role without unnecessary redundancy.
The tool set covers core workflows like searching, answering, and summarizing, but lacks update/delete operations for evidence and does not provide listing tools for guides or meetings, leaving notable gaps in lifecycle coverage.
Source-traced evidence research for AI agents. We organise the evidence; you decide.
Expert-structured medical ontologies, relationships, and evidence for AI research and reasoning.
Medical RAG: semantic search for clinical guidelines, drug interactions, diagnoses & EHR data.
Medical RAG: semantic search for clinical guidelines, drug interactions, diagnoses & EHR data.
