Mirobody
Lab results to LOINC codes and UCUM units, symptoms and diagnoses to ICPC-3. Offline, no API key.
Description from the maker (original English)
Drug & supplement lookup and interaction checks from free U.S. government health data.
These three are never merged into one score: opinions, measurements and popularity are different things.
We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.
Pick the AI app you use, then copy the settings.
MEDDATA_API_KEYValues marked secret are keys to your account. Never share them or paste them into a chat.
You need
Which app do you use?
Pick your app above. We remember it, so next time the button says “Install in …”.
Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json
{
"mcpServers": {
"meddata": {
"command": "uvx",
"args": [
"meddata-mcp"
],
"env": {
"MEDDATA_API_KEY": "your-value"
}
}
}
}Run this in your terminal
claude mcp add meddata --env MEDDATA_API_KEY=your-value -- uvx meddata-mcpFill in the empty values before running it.
Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"meddata": {
"command": "uvx",
"args": [
"meddata-mcp"
],
"env": {
"MEDDATA_API_KEY": "your-value"
}
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
Paste into .vscode/mcp.json in your project
{
"servers": {
"meddata": {
"type": "stdio",
"command": "uvx",
"args": [
"meddata-mcp"
],
"env": {
"MEDDATA_API_KEY": "your-value"
}
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.
Your choice stays on this device in a cookie; no account needed.
These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.
Runs on your computer; the tool list appears in your AI app after you install it.
We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026
The registry name was verified with the GitHub account anthesiallc, which owns the repo
We have not read this repo from GitHub yet
We have not read this repo from GitHub yet
This one runs on your computer; there is no internet endpoint
Runs on your computer and uses local settings instead
Runs on your computer; we can only read tool lists from internet endpoints
Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.
You need to fill in: MEDDATA_API_KEY
It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).
The GitHub repo last changed –, and the latest version (0.2.1) reached the registry 5 months ago.
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.
In the same category: Mirobody, FHIR MCP Server, Google Health MCP
Servers in the same category from other makers, sorted by stars.
Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
Lab results to LOINC codes and UCUM units, symptoms and diagnoses to ICPC-3. Offline, no API key.
MCP server providing seamless access to FHIR APIs for AI tools and healthcare applications
Privacy-first unofficial Google Health API v4 MCP server for health, sleep, activity, HR agents
Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.
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:
curl -X POST https://meddata.anthesia.io/api/v1/signup \
-H 'Content-Type: application/json' \ -d '{"email":"you@example.com"}'
The key comes back in the api_key field of the response.
The easiest way is with uv (no manual venv needed):
# stdio transport (default — for Claude Desktop, Cursor, most local clients)
MEDDATA_API_KEY=md_your_key uvx meddata-mcp
MEDDATA_API_KEY=md_your_key uvx meddata-mcp --http
Or install with pip into its own environment:
pip install meddata-mcp
MEDDATA_API_KEY=md_your_key meddata-mcp
Note: install into a dedicated environment. The
mcpSDK requires a newer
starlettethan 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):
{
"mcpServers": { "meddata": { "command": "uvx", "args": ["meddata-mcp"], "env": { "MEDDATA_API_KEY": "md_your_key" } } } }
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:
# pip install langchain-mcp-adapters langgraph "langchain[anthropic]"
from langchain_mcp_adapters.client import MultiServerMCPClient
client = MultiServerMCPClient( { "meddata": { "transport": "stdio", "command": "uvx", "args": ["meddata-mcp"], "env": {"MEDDATA_API_KEY": "md_your_key"}, } } ) tools = await client.get_tools()
tools to a LangGraph/LangChain agent, e.g.
LlamaIndex works the same way via its MCP tool spec.
git clone https://github.com/anthesiallc/meddata-mcp && cd meddata-mcp
python -m venv .venv .venv/Scripts/python -m pip install -e ".[http]" # Windows; [http] adds uvicorn for --http
MEDDATA_API_KEY=md_your_key .venv/Scripts/python -m meddata_mcp.server