Answers medication questions from the official FDA label using read-only, no-key MCP tools.
find_drug: resolve a brand/generic name, misspelling, or NDC through RxNorm; returns ingredients, brands, FDA classes, and manufacturer labels with set ids.
label_section: read one FDA label topic (boxed warning, indications, dosage, contraindications, warnings, interactions, pregnancy/lactation, populations, adverse reactions, overdosage, description, how supplied, clinical pharmacology) with label citation or an explicit no-section answer.
search_label: find every label paragraph mentioning a term such as another drug, grapefruit, alcohol, or a condition, with section heading and topic.
recalls_shortages: get FDA recalls and shortages for a drug, newest first.
Uses DailyMed, RxNorm/RxNav, and openFDA; read-only and not medical advice.
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., "@drug-label What are the boxed warnings for Eliquis?"
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.
Answers medication questions from the official FDA label, in the label's own words, with the label it came from: which manufacturer, which version, from when, and a link to read it on DailyMed. Also resolves any drug name, brand, misspelling or NDC through RxNorm, and reports FDA recalls and drug shortages.
A model asked about a drug answers from memory, and memory is out of date: labels change, boxed warnings are added, and interactions are revised. Drug Label reads the current label instead, and picks the right one: a common generic has hundreds of labels on DailyMed, most of them repackaged copies, so it chooses from manufacturers only (openFDA's original-packager index), with the brand's own NDA label first, and always says which label it used and how many others exist.
It is careful where mistakes are dangerous:
Look-alike names cause medication errors, so a misspelling is corrected only when one name is clearly meant. When two names are equally close, it refuses and asks which drug is meant.
A missing section is an explicit answer ("this label has no boxed warning section"), never a silent empty result.
Interaction answers are what each label says. NLM retired its drug-interaction API in 2024; this server does not pretend to replace it, and a label not mentioning an interaction does not mean there is none.
This is label information, not medical advice. Confirm anything that affects treatment with a pharmacist or prescriber.
No account or key needed. Built and maintained by Arhan Canli.
Needs Node.js 20 or newer. No account or key.
Claude Code
claude mcp add drug-label -- npx -y drug-label-mcpClaude Desktop: download drug-label-mcp-<version>.mcpb from the latest release and open it. The bundle is signed; verify it with gh attestation verify <file> --repo arhancanli/drug-label-mcp.
Any other client (Windsurf, Zed, Cline, Continue and others), in its MCP config file:
{
"mcpServers": {
"drug-label": {
"command": "npx",
"args": [
"-y",
"drug-label-mcp"
]
}
}
}Docker
docker build -t drug-label-mcp https://github.com/arhancanli/drug-label-mcp.git && docker run -i --rm drug-label-mcpHosted (Streamable HTTP): node src/server.mjs --http serves stateless MCP at POST /mcp (port from PORT, default 3000).
Related MCP server: Unofficial FDA MCP Server
An agent calls find_drug with:
{
"name": "atorvastatin"
}and gets back (recorded from the live server on 2026-09-26):
{
"rxcui": "83367",
"name": "atorvastatin",
"term_type": "IN",
"ingredients": [
"atorvastatin",
"atorvastatin / ezetimibe",
"amlodipine / atorvastatin"
],
"brands": [
"Lipitor",
"Atorvaliq",
"Caduet"
],
"classes": [
{
"name": "Hydroxymethylglutaryl-CoA Reductase Inhibitors",
"type": "MOA"
},
{
"name": "HMG-CoA Reductase Inhibitor",
"type": "EPC"
}
],
"labels_total": 25,
"labels": [
{
"setid": "a60cc18b-0631-4cf0-b021-9f52224ece65",
"brand": "Lipitor",
"generic": "atorvastatin calcium",
"manufacturer": "Viatris Specialty LLC",
"application": "NDA020702",
"category": "NDA",
"form": "TABLET, FILM COATED",
"since": "2024-05-01"
},
{
"setid": "d9adb9e4-c495-9530-e5e3-7e3b01d53e4c",
"brand": "ATORVASTATIN CALCIUM",
"generic": "ATORVASTATIN CALCIUM",
"manufacturer": "Apotex Corp.",
"application": "ANDA090548",
"category": "ANDA",
"form": "TABLET, FILM COATED",
"since": "2012-05-29"
},
{
"setid": "901550e6-22be-68c9-eb86-f85a9c9dd998",
"brand": "Atorvastatin Calcium",
"generic": "Atorvastatin Calcium",
"manufacturer": "Dr. Reddy's Laboratories Limited",
"application": "ANDA091650",
"category": "ANDA",
"form": "TABLET",
"since": "2012-07-17"
},
{
"setid": "6ccdb6f3-22c7-5b48-46bc-ce4a4c65eb4d",
"brand": "Atorvastatin Calcium",
"generic": "Atorvastatin Calcium",
... (18 more lines)Tool | What it does |
| Resolves a brand or generic name, misspelling or NDC through RxNorm: ingredients, brands, FDA classes, and the best manufacturer labels (not repackagers) with set ids. Never guesses between look-alike names. |
| Returns one topic of the official FDA label as the label's own text under its headings, citing the label's set id, version and date. Says so when the label has no such section. |
| FDA drug recalls (class, reason, status, date, product) and shortage records (status, company, reason) for a drug, newest first, from openFDA. |
| Finds every paragraph of the official FDA label that mentions a term (another drug, grapefruit, alcohol, a condition), with its section heading and topic. |
Topics for label_section: boxed_warning, indications, dosage, contraindications,
warnings, interactions, pregnancy_lactation, specific_populations, adverse_reactions,
overdosage, description, how_supplied, clinical_pharmacology. Each maps to the FDA's section
codes, including the older WARNINGS and PRECAUTIONS layout and OTC Drug Facts labels.
Read-only: no tool changes anything outside this process.
Network: HTTPS only, to the hosts listed in package.json under factory.allowHosts, with a
deadline, a size cap and bounded retries; a stalled read is retried. Nothing else is contacted, and
nothing is logged except unexpected failures (to stderr, without your inputs).
Keeps to each source's limits (RxNav asks for at most 20 requests per second; openFDA allows 240 per minute without a key) and caches for 12 hours, as NLM asks. Parsed labels are kept in memory.
Label text keeps lists as items and tables row by row, repeating a cell that spans rows so each row reads on its own. Long answers are cut at 12,000 characters with a count of what was left out.
Results are compact JSON with a matching output schema.
Measured 2026-09-26 with gpt-5.4-mini, 10 fixed tasks graded by fixed checks (bench/tasks.json, raw results in bench/results/).
Server | Correct | Input tokens | Output tokens | Tool calls | Median time |
This server | 10/10 | 23729 | 459 | 14 | 4.1 s |
@ythalorossy/openfda, the most downloaded openFDA server | 7/10 | 118095 | 643 | 14 | 2.2 s |
Measured 2026-09-26 from Dubai, home connection against the live upstream, Node 24.19.0 (bench/perf.json, scripts/perf.mjs in the factory).
Call | First call | Repeat | Result size |
find_drug: atorvastatin (brand and generic manufacturers ranked) | 3673 ms | 1.2 ms | 1,560 chars |
find_drug: a misspelled name | 4053 ms | 0.9 ms | 1,591 chars |
label_section: warfarin boxed warning | 4171 ms | 0.5 ms | 1,437 chars |
label_section: Lipitor boxed warning (none) | 4192 ms | 0.3 ms | 508 chars |
search_label: Lipitor and grapefruit | 4694 ms | 0.4 ms | 3,766 chars |
label_section: metformin contraindications | 4468 ms | 0.6 ms | 1,257 chars |
recalls_shortages: metformin | 2352 ms | 0.7 ms | 4,766 chars |
First call: a fresh server process, including the TLS connection and the upstream's own time. Repeat: the same call again, answered from the in-process cache, so it shows this server's own overhead.
Tool definitions the model reads on every turn (name, description, input schema): 1,958 characters, against 23,915 for @ythalorossy/openfda, the most downloaded openFDA server. The full tool list, with the output schemas and annotations clients use to validate results, is 3,819 characters (24,351 for the alternative).
DailyMed (National Library of Medicine): FDA label text (SPL).
RxNorm through RxNav: drug names, ingredients, brands, NDCs, FDA pharmacologic classes. Only RxNorm's own content is used.
openFDA: the NDC directory (to find manufacturers' labels), recall enforcement reports and drug shortages.
All three are US government services. They do not endorse this server.
Actions Check: Checks GitHub Actions workflows: outdated actions, old Node runtimes, retired runners, injection.
Citation Check: Verifies citations: finds fabricated or mismatched references and retractions, returns clean BibTeX.
Config Check: Validates config files against their official schemas: tsconfig, compose, workflows, 1,400+ more.
Contact Check: Validates and formats phone numbers, email addresses and postal addresses for any country.
Cron Check: Explains cron expressions, lists next run times in any time zone, converts between cron dialects.
Dockerfile Check: Checks Dockerfiles: build-breaking mistakes, base image tags that exist, digests, platforms, EOL.
Domain Health: Email and domain checks: SPF lookup limits, DKIM keys, DMARC, DNS records, registration expiry.
End of Life: Is this version still supported? EOL dates, latest patch and upgrade target for 470+ products.
The whole collection, 12 more
MIT, Copyright (c) 2026 Arhan Canli.
Resolves a brand or generic name, misspelling or NDC through RxNorm: ingredients, brands, FDA classes, and the best manufacturer labels (not repackagers) with set ids. Never guesses between look-alike names.
Inputs: name (required). Returns: name, rxcui, labels
Returns one topic of the official FDA label as the label's own text under its headings, citing the label's set id, version and date. Says so when the label has no such section.
Inputs: topic (required), drug, setid. Returns: found, label, topic, sections
FDA drug recalls (class, reason, status, date, product) and shortage records (status, company, reason) for a drug, newest first, from openFDA.
Inputs: drug (required). Returns: drug, recalls_total, shortages_total
Finds every paragraph of the official FDA label that mentions a term (another drug, grapefruit, alcohol, a condition), with its section heading and topic.
Inputs: term (required), drug, setid. Returns: term, label, total, matches
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
Scored across 4 tools
Each tool targets a distinct concern: drug name/NDC resolution (find_drug), retrieving a specific label section (label_section), obtaining recalls/shortages (recalls_shortages), and full-text label searching (search_label). There is no overlap or ambiguity about which tool to use for a given task.
All names use lowercase snake_case and are descriptive, but they mix verb-first (find_drug, search_label) and noun-first (label_section, recalls_shortages) patterns. This is a minor deviation from a strictly consistent verb_noun convention, but the naming remains predictable and readable.
With only 4 tools, the server is tightly scoped to the core needs of FDA drug label information: identification, label access, searching, and safety alerts. This is well within the 3–15 tool range and each tool earns its place without redundancy.
The tool surface covers the essential lifecycle: find a drug, retrieve a section, search across the label, and check recalls/shortages. A minor gap is the lack of a way to list all available sections or retrieve the full label at once, but the existing tools combined can achieve most user needs without failure.
Official FDA recalls, drug labels, adverse events and clinical trials, with cited sources.
US medication lookup: drug search, forms and strengths, NDC/RxCUI, recalls, shortages.
Test healthcare AI agents' medication claims against cited FDA label text. Not for clinical use.
Medicine names, label text, safety reports, recalls, shortages and approvals from regulators
