The full upstream README, mirrored here for reference. Install config, tool schemas, adoption signals, and an original overview live on the Meddata listing page.
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.
| Tool | What it does |
|---|---|
search_drugs | Search drugs by brand or generic name; returns RxCUI + details |
get_drug | Full drug profile by RxCUI |
get_drug_by_ndc | Drug profile by NDC package code |
search_supplements | Search supplements by name; returns supplement IDs |
get_supplement | Full supplement fact sheet by ID |
check_interactions | Interactions across a mixed list of 2-10 drugs/supplements |
assess_routine | Full assessment of a routine: per-pair coverage, sources, timing and food conditions |
get_usage | Current billing period usage and plan limit |
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:
| status | means |
|---|---|
finding | one or more grounded findings, listed in findings[] |
no_finding_in_sources | a source covering this kind of pair was consulted and had nothing. Absence of a finding is not evidence of safety |
not_covered | no source covers this kind of pair, so it was not assessed. Most supplement-supplement pairs land here |
unresolved | an item did not resolve, so nothing could be checked |
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.
Free tier is 250 calls/month, no credit card:
The key comes back in the api_key field of the response.
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 to https://meddata.anthesia.io.MEDDATA_TIMEOUT (optional) — request timeout in seconds, default 30.Add to claude_desktop_config.json (Settings → Developer → Edit Config):
Add the same block to ~/.cursor/mcp.json (or the project .cursor/mcp.json).
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.
Any LangChain or LangGraph agent can use these tools through
langchain-mcp-adapters:
LlamaIndex works the same way via its MCP tool spec.