Four pharma workflows your AI agent can ground in primary sources
Safety-signal lookups, trial-landscape scans, and literature monitoring — each run live against openFDA, ClinicalTrials.gov, and PubMed through one MCP endpoint, with the source in every answer.
Pharma teams were early to feel the sharpest failure mode of ungrounded AI: answers that look sourced. A reference list with plausible PMIDs, a trial with a well-formed NCT number — neither of which exists. For medical affairs, drug safety, and publications work, an uncited answer isn’t a time-saver; it’s a liability.
The fix is boring and effective: let the agent query the primary source at answer time. Below are four workflows we watched run end-to-end on live data (July 2026), each a real question from a real role, answered by an AI agent connected to Pipeworx. Every response carries the query it ran and the source it hit.
1. Drug safety: FAERS reaction profiles
The question: “What are the most frequently reported adverse reactions for semaglutide in FAERS?”
The agent routes this to openFDA’s adverse-event endpoint and runs the count query itself. As of this writing: nausea (12,084 reports), vomiting (7,988), off-label use (7,322), diarrhoea (6,943), decreased appetite, constipation — the full MedDRA reaction profile, with the exact openFDA query string in the response. Reproducible, auditable, and refreshed on every ask.
Same pattern for seriousness breakdowns (“how many amiodarone reports involved hospitalization?” — 5,410 hospitalization-flagged reports) and age distributions. To be clear about the division of labor: this is the hour of lookups before the safety scientist’s judgment call — not signal detection, and not a conclusion.
2. Label checks: what does the current label actually say?
The question: “Does the current FDA label for clozapine carry a boxed warning, and what does it warn about?”
The agent pulls the current structured product label from openFDA — effective date March 2026 — and reads back the actual warning text: severe neutropenia, seizure risk, the works. Not a paraphrase from training data that may predate three label revisions; the label as filed.
3. Clinical ops: the recruiting landscape, queried live
The question: “What clinical trials are currently recruiting for GLP-1 receptor agonists in adolescents?”
ClinicalTrials.gov, queried at answer time: six recruiting studies, each with its NCT id, phase, sponsor, enrollment target, and interventions — for example NCT05819853, a Phase 3 semaglutide study in youth and adults with PCOS, estimated enrollment 80. Feasibility triangulation that usually means an afternoon of registry tabs, in one question.
4. Literature monitoring: PMIDs you can actually cite
The question: “What are the most recent publications on semaglutide cardiovascular outcomes?”
PubMed returns 879 matches; the agent surfaces the latest with titles, journals, dates, and PMIDs — identifiers a medical writer can drop straight into a reference manager. Filtered searches work too: restricting to randomized controlled trials of GLP-1 agonists for weight loss returns 475 publication-type-filtered results.
The difference from asking a chatbot for “recent papers” is the failure mode. A grounded agent that finds nothing says so. An ungrounded one invents a reference that gets caught in review — or doesn’t.
Wire it up
All four workflows run through one MCP endpoint:
https://gateway.pipeworx.io/pipeworx-catalog/mcpAdd it to Claude, ChatGPT, Cursor, or your own agent — the getting-started
guides cover each client. No key required to try it; the
agent’s ask_pipeworx tool routes plain-English questions to openFDA, ClinicalTrials.gov,
PubMed, and 1,200+ other live sources, and every answer comes back with its source attached.