💊 drug-pipeline-mcp

Pharmaceutical R&D Pipeline Intelligence for AI Agents — Clinical trials, FDA/EMA approvals, safety data, drug interactions, drug labels, recalls, patents & publications in one MCP server.

No hallucination. Every output traces to a source NCT ID, FDA application number, or PMID.

Quick Start

pip install git+https://github.com/DasClown/drug-pipeline-mcp.git
# or try it on Smithery: https://smithery.ai/servers/crop-mcp/drug-pipeline

# Start MCP server (stdio)
drug-pipeline

# Or HTTP mode for remote access
pip install drug-pipeline-mcp[http]
drug-pipeline --http --port 8081

Tools (17)

Example Agent Queries

"What's in the pipeline for GLP-1 agonists?" → drug_pipeline(drug_name="semaglutide") → ATC class, FDA status, 10+ trials, publications

"Which companies have Phase 3 trials for non-small cell lung cancer?" → search_trials(condition="non-small cell lung cancer", phase="PHASE3", status="RECRUITING")

"Is pembrolizumab approved in the EU vs US?" → get_approvals(drug_name="Keytruda") → FDA submission history with dates → get_eu_approvals(drug_name="Keytruda") → EU authorization status

"What are the safety signals for semaglutide?" → get_safety_data(drug_name="semaglutide") → 6,027 FAERS reports, top reactions: Nausea (862), Vomiting (750) → detect_safety_signals(drug_name="semaglutide") → PRR analysis, disproportionate signals

"What does the label say for Keytruda?" → get_drug_label(drug_name="Keytruda") → indications, boxed warnings, contraindications, dosing

"Are there recalls for Tylenol?" → get_recalls(drug_name="Tylenol") → recall dates, reasons, classification, firms

"When does the patent for Keytruda expire?" → get_patent_expiry(drug_name="Keytruda") → approval dates, exclusivity information

"What drugs are approved for non-small cell lung cancer?" → approved_for_condition(condition="non-small cell lung cancer") → 82 drugs (Keytruda, Tagrisso, Opdivo, Tecentriq, ...)

"What are the eligibility criteria for NCT03178617?" → get_trial_detail(nct_id="NCT03178617")

Example Output (drug_pipeline)

{
  "status": "ok",
  "query": {"drug_name": "semaglutide"},
  "drug_info": {
    "atc_classification": {"code": "A10BJ", "name": "GLP-1 analogues"},
    "rxcui": "1991302",
    "products": [{"brand_name": "Ozempic", "generic_name": "semaglutide", "labeler": "Novo Nordisk"}]
  },
  "clinical_trials": { "results": [ ... ] },
  "publications": { "total_count": 846, "returned_count": 5 },
  "data_sources": ["openFDA", "RxNorm", "PubMed", "clinicaltrials.gov"]
}

Architecture

drug-pipeline-mcp/
├── drug_pipeline/
│   ├── __init__.py        # Version
│   ├── server.py          # MCP server (16 tools)
│   └── sources.py         # Data source fetchers
├── drug_pipeline_cli.py   # CLI entry point
├── pyproject.toml
└── README.md

No machine learning. No predictions. Only structured synthesis of verified primary sources.

Data Sources

Anti-Hallucination

Every result includes:

• NCT ID → https://clinicaltrials.gov/study/NCT...
• FDA Application Number → https://www.accessdata.fda.gov/...
• PMID → https://pubmed.ncbi.nlm.nih.gov/PMID...

No calculated fields, no predictions, no "ungefähre" estimates.

Language

All output is in English (JSON field names, descriptions, results). The server can be configured via the language parameter on Smithery for future localization support.

License

MIT