Drug Intelligence MCP Server

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AI agents for pharmaceutical research — FDA drug data for drug intelligence, adverse event monitoring, drug interaction checking, and pharmaceutical due diligence.

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1. Purpose Statement

Drug Intelligence MCP is an MCP (Model Context Protocol) server that gives AI agents access to FDA pharmaceutical data via openFDA APIs. AI agents performing drug research, pharma intelligence, clinical decision support, or healthcare workflows can query real-time FDA drug labels, adverse events, NDC codes, drug interactions, and recall data without requiring API keys or manual database navigation.

Built for: AI agents doing drug research, pharma intelligence, clinical decision support, healthcare compliance, drug safety monitoring, and pharmaceutical due diligence.

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2. Quick Start

Add to your MCP client:

{
  "mcpServers": {
    "drug-intelligence-mcp": {
      "url": "https://red-cars--drug-intelligence-mcp.apify.actor/mcp"
    }
  }
}

AI agents can now search FDA drug data, check adverse events, look up NDC codes, verify drug interactions, and track recalls.

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3. When to Call This MCP

Use Drug Intelligence MCP when you need to:

• Research drug labeling — Get indications, dosage, warnings, and active ingredients for any FDA-approved medication
• Monitor drug safety — Search adverse event reports to identify safety signals
• Verify NDC codes — Look up National Drug Codes for pharmaceutical verification
• Check drug interactions — Verify drug-drug interactions before prescribing
• Track drug recalls — Monitor FDA drug recalls by manufacturer or product
• Pharma due diligence — Research drug safety profiles for investment or procurement decisions
• Clinical decision support — Access FDA labeling data for clinical decision making
• Formulary verification — Confirm drug availability and NDC codes for insurance/payer systems

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4. What Drug Intelligence Data Can You Access?

Data Type	Source	Example
Drug Labels	FDA openFDA	Indications, dosage, warnings, active ingredients
Adverse Events	FDA openFDA	Reported safety signals, serious outcomes
NDC Codes	FDA openFDA	National Drug Code verification
Drug Interactions	FDA openFDA	Drug-drug interaction descriptions
Drug Recalls	FDA openFDA	Class I/II/III recall notices

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5. Why Use Drug Intelligence MCP?

The problem: Drug research — FDA approvals, adverse events, NDC codes, drug interactions, recalls — requires searching multiple government databases and synthesizing findings into actionable intelligence. For clinical decision support, pharma due diligence, and healthcare AI agents, this data is essential for drug safety monitoring, formulary verification, and regulatory compliance. Manual research takes hours across disconnected FDA systems.

The solution: AI agents use Drug Intelligence MCP to get instant, structured pharmaceutical intelligence on any FDA-approved drug — the FDA data layer for drug research and clinical decision support workflows.

Key benefits:

• Drug label search — Search across all FDA-approved drug labeling information
• Adverse event monitoring — Search adverse event reports across all drug categories
• NDC code lookup — Verify National Drug Codes for pharmaceutical products
• Drug interaction checking — Identify potential drug-drug interactions
• Recall tracking — Monitor Class I/II/III recalls by drug or manufacturer
• No API key required — Uses free openFDA APIs, works immediately
• Parallel data fetching — Fast responses for time-sensitive drug research

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6. Features

Comprehensive FDA Coverage Access all major FDA pharmaceutical databases: drug labels, adverse events, NDC codes, and recall reports.

Clinical Decision Support Search drug labeling for indications, dosage forms, warnings, contraindications, and active ingredients.

Drug Safety Intelligence Monitor adverse event reports for safety signals, serious outcomes, and patient demographics.

NDC Verification Look up exact National Drug Codes for formulary verification, insurance processing, and pharmaceutical authentication.

Drug Interaction Checking Identify potential drug-drug interactions and contraindications from FDA labeling.

Recall Monitoring Track FDA drug recalls by drug name, manufacturer, classification, or date range.

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7. How It Compares to Alternatives

Aspect	Our MCP	GoodRx	DrugBank
Price	$0.03-$0.08/call	Subscription required	$15,000+/year
API access	MCP (AI-native)	REST (limited)	REST (expensive)
Tool coverage	5 tools (labels, events, NDC, interactions, recalls)	Limited drug data	Full database
Data source	FDA openFDA (free)	Aggregated	Licensed
AI agent integration	Native MCP protocol	No MCP support	No MCP support
No API key	Yes	No	No
Adverse events	Yes	No	Yes
Recall tracking	Yes	No	Limited

Why choose our MCP:

• MCP protocol is designed for AI agent integration — call drug intelligence tools with natural language
• Free FDA openFDA data source — no API key, no registration, no approval required
• DrugBank costs $15,000+/year for enterprise — our MCP is fractions of a cent per call
• GoodRx has no MCP support — our MCP works natively with Claude, Cursor, and other AI clients
• Drug interaction checking from FDA labeling data — clinical-grade information
• Recall monitoring for pharmaceutical due diligence and drug safety

Competitor APIs:

• GoodRx: https://www.goodrx.com/developers (requires approval, subscription-based)
• DrugBank: https://dev.drugbank.com (enterprise pricing, $15,000+/year)
• FDA openFDA: https://open.fda.gov/ (free, but requires implementation work)

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8. Use Cases for Drug Intelligence

Drug Label Research

Persona: Clinical pharmacist using AI to verify drug information

AI agent: "Get the drug label information for metformin including indications, dosage, and warnings"
MCP call: search_drug_labels({ drug_name: "metformin", max_results: 5 })
Returns: brand_name, generic_name, indications, dosage_forms, warnings, active_ingredients

Adverse Event Monitoring

Persona: Pharmacovigilance specialist monitoring drug safety

AI agent: "Search for adverse event reports on warfarin in the last 6 months, focus on serious outcomes"
MCP call: get_drug_adverse_events({ drug_name: "warfarin", seriousness: "serious", max_results: 20 })
Returns: report_id, receive_date, serious_outcomes, reactions, drug_indication

NDC Code Verification

Persona: Healthcare IT specialist verifying pharmaceutical codes

AI agent: "Look up the NDC code for Lipitor 20mg tablets"
MCP call: search_drug_by_ndc({ drug_name: "Lipitor", max_results: 5 })
Returns: ndc_code, brand_name, generic_name, labeler_name, dosage_form, active_ingredients

Drug Interaction Checking

Persona: Clinical decision support system checking prescriptions

AI agent: "Check if there are any known drug interactions between warfarin and aspirin"
MCP call: get_drug_interactions({ drug1: "warfarin", drug2: "aspirin" })
Returns: interactions with severity, description, clinical_effects, management

Recall Tracking

Persona: Pharmacy manager monitoring drug recalls

AI agent: "Find all FDA Class I recalls on blood pressure medications in the last year"
MCP call: search_drug_recalls({ drug_name: "lisinopril", classification: "Class I", max_results: 10 })
Returns: recall_id, drug_name, recalling_firm, classification, reason_for_recall, recall_date

Pharmaceutical Due Diligence

Persona: Biotech analyst researching drug safety profiles

AI agent: "Generate a drug safety profile for the GLP-1 agonist semaglutide including adverse events and recalls"
MCP calls: search_drug_labels({ drug_name: "semaglutide" })
          + get_drug_adverse_events({ drug_name: "semaglutide" })
          + search_drug_recalls({ drug_name: "semaglutide" })
Returns: complete drug profile with labels, events, and recall data

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9. How to Connect Drug Intelligence MCP Server to Your AI Client

Step 1: Get your Apify API token (optional)

Sign up at apify.com and copy your API token from the console. The MCP works without an API token for tool calls, but Apify authentication may be required by some MCP clients.

Step 2: Add the MCP server to your client

Claude Desktop: Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "drug-intelligence-mcp": {
      "url": "https://red-cars--drug-intelligence-mcp.apify.actor/mcp"
    }
  }
}

Cursor/Windsurf: Add to MCP settings:

{
  "mcpServers": {
    "drug-intelligence-mcp": {
      "url": "https://red-cars--drug-intelligence-mcp.apify.actor/mcp"
    }
  }
}

Step 3: Start querying

AI agent: "Check for drug interactions between lisinopril and potassium supplements"

Step 4: Retrieve results

The MCP returns structured JSON with drug data, adverse events, NDC codes, or recall information.

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10. MCP Tools

Tool	Price	Description
search_drug_labels	$0.03	Search FDA drug label database for medication labeling information
get_drug_adverse_events	$0.08	Search FDA adverse event reporting system for drug safety signals
search_drug_by_ndc	$0.03	Look up drug information by National Drug Code (NDC)
get_drug_interactions	$0.05	Check drug-drug interactions and contraindications
search_drug_recalls	$0.03	Search FDA drug recall database for medication recalls

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11. Tool Parameters

search_drug_labels

Parameter	Type	Required	Description
drug_name	string	No	Drug name (e.g., 'aspirin', 'ibuprofen', 'metformin')
indication	string	No	Therapeutic indication or condition
max_results	integer	No	Maximum results (default: 10)

When to call: Persona: Clinical pharmacist or healthcare AI agent. Scenario: "I need to verify the indications and warnings for a medication before dispensing."

Example AI prompt: "Get the drug label information for metformin including its indications for Type 2 diabetes, recommended dosage, and any black box warnings."

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get_drug_adverse_events

Parameter	Type	Required	Description
drug_name	string	No	Drug name
seriousness	string	No	Seriousness filter (serious, death, disability, etc.)
date_from	string	No	Start date YYYYMMDD
date_to	string	No	End date YYYYMMDD
max_results	integer	No	Maximum results (default: 10)

When to call: Persona: Pharmacovigilance specialist or drug safety AI. Scenario: "Monitor adverse event reports for a drug to identify safety signals."

Example AI prompt: "Search FDA adverse event reports for pembrolizumab (Keytruda) in the last 12 months, focusing on serious outcomes like death or life-threatening reactions."

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search_drug_by_ndc

Parameter	Type	Required	Description
ndc_code	string	No	10 or 11-digit NDC code (e.g., '0069-1530-30')
drug_name	string	No	Drug name (alternative to NDC code)
max_results	integer	No	Maximum results (default: 10)

When to call: Persona: Healthcare IT specialist or pharmacy system AI. Scenario: "Verify the NDC code for a specific pharmaceutical product for formulary or insurance processing."

Example AI prompt: "Look up the NDC code and manufacturer information for Eliquis (apixaban) 5mg tablets to verify for insurance prior authorization."

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get_drug_interactions

Parameter	Type	Required	Description
drug1	string	Yes	First drug name
drug2	string	Yes	Second drug name
max_results	integer	No	Maximum results (default: 10)

When to call: Persona: Clinical decision support AI or pharmacist. Scenario: "Check for drug-drug interactions before a prescription is finalized."

Example AI prompt: "Check if there are any known drug interactions between warfarin and amiodarone. What is the severity and clinical management guidance?"

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search_drug_recalls

Parameter	Type	Required	Description
drug_name	string	No	Drug name
recalling_firm	string	No	Recalling firm/manufacturer
classification	string	No	Recall classification (Class I, II, III)
reason	string	No	Reason for recall
date_from	string	No	Start date YYYYMMDD
date_to	string	No	End date YYYYMMDD
max_results	integer	No	Maximum results (default: 10)

When to call: Persona: Pharmacy manager or drug safety AI. Scenario: "Check for active Class I or II recalls on blood pressure medications before dispensing."

Example AI prompt: "Find all FDA Class I recalls on ACE inhibitors (like lisinopril or enalapril) in the last 2 years and show the reason for each recall."

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12. Connection Examples

cURL

curl -X POST "https://red-cars--drug-intelligence-mcp.apify.actor/mcp" \
  -H "Authorization: Bearer YOUR_APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "tool": "search_drug_labels",
    "params": { "drug_name": "metformin", "max_results": 5 }
  }'

Node.js

const response = await fetch('https://red-cars--drug-intelligence-mcp.apify.actor/mcp', {
  method: 'POST',
  headers: {
    'Authorization': 'Bearer YOUR_APIFY_TOKEN',
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({
    tool: 'search_drug_labels',
    params: { drug_name: 'metformin', max_results: 5 }
  })
});
const data = await response.json();
console.log(data.result.drugs[0].indications);

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13. Output Example

{
  "status": "success",
  "result": {
    "query": { "drug_name": "metformin", "max_results": 5 },
    "total_results": 125,
    "drugs": [
      {
        "brand_name": "Glucophage",
        "generic_name": "metformin hydrochloride",
        "ndc_codes": ["0378-0224-01", "0378-0225-01"],
        "indications": "Glucophage (metformin hydrochloride) is indicated as an adjunct to diet and exercise to improve glycemic control in adults and children with type 2 diabetes mellitus.",
        "dosage_forms": ["Tablet"],
        "manufacturer": "Mylan Pharmaceuticals Inc.",
        "active_ingredients": ["metformin hydrochloride"],
        "warnings": ["Lactic acidosis is a rare but serious metabolic complication.", "Should not be used in patients with renal disease."],
        "dosage_and_administration": "The usual starting dose is 500 mg twice daily or 850 mg once daily."
      }
    ],
    "source": "FDA Drug Label"
  }
}

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14. Output Fields

Field	Description
query	The original search parameters
total_results	Total matching records in FDA database
drugs/events/recalls	Array of matching records
source	Data source (FDA Drug Label/Adverse Event/NDC/Recall)
ndc_code	National Drug Code (11-digit format)
brand_name	FDA established name
generic_name	Active ingredient name
classification	Recall class (I, II, III)

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15. How Much Does It Cost to Run Drug Intelligence MCP?

PPE (Pay-Per-Event) pricing — $0.03 to $0.08 per tool call.

Tool	Price
search_drug_labels	$0.03
get_drug_adverse_events	$0.08
search_drug_by_ndc	$0.03
get_drug_interactions	$0.05
search_drug_recalls	$0.03

No subscription. No monthly fee. Pay only when AI agents use the tools.

FDA openFDA is free — we charge for the MCP infrastructure and AI agent integration, not the underlying data.

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16. How Drug Intelligence MCP Works

Phase 1: Request parsing

AI agent sends tool call via MCP protocol. Server parses tool name and parameters.

Phase 2: FDA API query

For each tool, the server constructs the appropriate openFDA API query:

• Drug label: /drug/label.json with search parameters
• Adverse events: /drug/event.json with drug name and date filters
• NDC lookup: /drug/ndc.json with product NDC or drug name
• Drug interactions: Searches drug labels for both drugs, extracts interaction info
• Drug recalls: /drug/recall.json with drug/firm/classification filters

Phase 3: Response formatting

All results returned as structured JSON with normalized field names and source attribution.

Phase 4: Pricing

PPE charges applied via Apify Actor.charge() for cost tracking.

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17. Tips for Best Results

1. Use specific drug names — More specific queries (e.g., "metformin hydrochloride") return better results than generic ("metformin")

2. Include brand names when known — Searching both brand and generic names improves recall

3. Filter by date for trending — Use date_from/date_to to track adverse events or recalls over specific time periods

4. Use NDC codes for verification — NDC codes are the gold standard for pharmaceutical product identification

5. Check interactions before prescribing — Use get_drug_interactions for clinical decision support

6. Monitor recalls regularly — Set up recurring checks for Class I and II recalls on critical medications

7. Adverse events ≠ causation — FDA adverse event reports indicate suspicion, not confirmed causation

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18. Combine with Other Apify Actors

For comprehensive healthcare and pharma intelligence:

• healthcare-compliance-mcp — Medical device compliance data (device ↔ drug overlap)
• academic-research-mcp — Drug literature search and academic papers
• tech-scouting-report-mcp — Pharma R&D momentum and patent intelligence

Research chain:

Drug Intelligence MCP → healthcare-compliance-mcp → academic-research-mcp

AI agents researching a drug can: (1) check FDA labeling and adverse events, (2) verify medical device interactions, (3) find academic literature on clinical trials.

Note: Drug Intelligence MCP provides FDA regulatory data (drug labels, adverse events, recalls). For clinical decision support, always verify with healthcare professionals. No API key required — AI agents can call these tools directly.

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SEO Keywords

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License

Apache 2.0