
Meddata
io.github.anthesiallc由 GitHub 账号 anthesiallc 发布v0.2.1更新于 Oct 24, 2026
Drug & supplement lookup and interaction checks from free U.S. government health data.
概览
让助手查询美国药品与补充剂数据,并检查一组药品和补充剂之间的相互作用。
- 可以做什么
- 将 MedData REST API 封装为 MCP 工具。工具包括按品牌或通用名搜索药品的 search_drugs、按 RxCUI 获取药品详情的 get_drug、按 NDC 编码查询的 get_drug_by_ndc、搜索补充剂的 search_supplements、获取补充剂说明的 get_supplement、检查 2 至 10 项混合清单相互作用的 check_interactions、给出逐对评估及来源、服用时间和食物条件的 assess_routine,以及查询计费周期用量的 get_usage。assess_routine 会区分有发现、已查来源但无发现、无来源覆盖和未能解析四种情况。
- 适用场景
- 适合需要以对话方式查询药品或补充剂信息,或需要带来源依据的相互作用检查的场景。assess_routine 适合结果需要解释或据此行动的情况;check_interactions 适合只需快速列出结果的情况。
- 运行要求
- 需要本地 Python 运行环境(PyPI 包 meddata-mcp,可用 uvx 或 pip 运行),并在 MEDDATA_API_KEY 环境变量中提供 MedData API 密钥;可申请免费密钥,每月 250 次调用。可选 MEDDATA_BASE_URL 和 MEDDATA_TIMEOUT。由于与 MedData API 应用存在依赖冲突,应安装到独立环境。
安装
在 SourceWeft 中
- 打开 控制台中的 Meddata,将其添加到工作区。
- 为需要使用其工具的对话启用该服务。
Desktop only,通过 STDIO。 STDIO 服务会启动本地进程,因此需要 SourceWeft 桌面宿主。
其他 MCP 客户端
参照 仓库 中的启动说明。
README
MedData MCP Server
mcp-name: io.github.anthesiallc/meddata
A Model Context Protocol server that exposes the MedData API as tools, so any MCP client (Claude Desktop, Cursor, ChatGPT connectors, or an agent framework) can look up drug and supplement data and check interactions conversationally.
It's a thin wrapper: each tool maps to one MedData REST endpoint. All the data work happens in the API.
Tools
check_interactions vs assess_routine
check_interactions returns a flat list of interactions. assess_routine
returns the reasoning: what each submitted name resolved to, what was consulted
for every pair, and the document behind each finding. Reach for it when the
answer will be explained or acted on rather than just displayed.
The distinction that matters most is in pairs_checked[].status:
not_covered and no_finding_in_sources are different answers, and a flat
empty list cannot tell them apart.
assess_routine also takes class_matching (default off). With it on, a drug
matches the therapeutic classes a supplement record names, not only the drug
names it lists; those findings carry basis.level: "class" and name the class,
so an inferred match stays distinguishable from an explicit one. Findings also carry basis.level
(formulation / ingredient / class) saying how closely the source matches
what was submitted, three-state timing and food conditions where silence is
not_documented rather than "no effect", and references[] with the document
version and the verbatim passage.
Get an API key
Free tier is 250 calls/month, no credit card:
The key comes back in the api_key field of the response.
Install and run
The easiest way is with uv (no manual venv needed):
Or install with pip into its own environment:
Note: install into a dedicated environment. The
mcpSDK requires a newerstarlettethan the MedData API app pins, so the two will conflict if installed together.
Environment variables:
MEDDATA_API_KEY(required) — your MedData API key.MEDDATA_BASE_URL(optional) — defaults tohttps://meddata.anthesia.io.MEDDATA_TIMEOUT(optional) — request timeout in seconds, default30.
Client configuration
Claude Desktop
Add to claude_desktop_config.json (Settings → Developer → Edit Config):
Cursor
Add the same block to ~/.cursor/mcp.json (or the project .cursor/mcp.json).
Smithery (hosted, no install)
The server is hosted on Smithery, so MCP clients that support Smithery can connect without installing anything. You provide your MedData API key in the Smithery config and it routes to the server.
LangChain / LangGraph
Any LangChain or LangGraph agent can use these tools through
langchain-mcp-adapters:
LlamaIndex works the same way via its MCP tool spec.
Develop from source
Notes
- Data is for informational purposes only and is not medical advice.
- Interaction data comes from established medical databases; an empty result means none were found in those sources, not that a combination is proven safe.
来源:README.md,提交 a0ab4de
工具
0版本历史
1- v0.2.1最新Sep 16, 2026
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