Using AI to Analyze FDA Compliance Data: A Developer's Guide

FDA compliance data is vast, fragmented, and constantly updating. Recalls, warning letters, inspection outcomes, 510(k) clearances, and facility registrations live across separate databases with different schemas, naming conventions, and update cadences. Manually stitching this data together for regulatory intelligence, due diligence, or competitive analysis is tedious and error-prone.

What if you could ask an AI agent — in plain English — to look up a company's FDA compliance history, find recent Class I recalls, or compare 510(k) clearance trends across competitors? That's exactly what FDA Data MCP enables through the Model Context Protocol (MCP), an open standard that connects AI agents to external data sources.

This guide walks you through setup, configuration, and practical use cases — from your first query to advanced multi-tool workflows.

1. What Is MCP and Why It Matters for FDA Data

The Model Context Protocol (MCP) is an open standard created by Anthropic that lets AI assistants connect to external tools and data sources. Instead of training a model on static data, MCP gives the AI live, structured access to real-time information.

For FDA data, this is transformative. Rather than relying on an AI model's training data (which is always stale and often inaccurate for specific regulatory facts), MCP lets the AI query the actual FDA databases in real time:

• Live data: Queries hit our database, which syncs with FDA sources daily. No stale training data.
• Structured results: The AI gets clean, normalized JSON — not web-scraped text. This means accurate counts, proper dates, and consistent company names.
• Cross-referencing: A single query can pull data from multiple FDA datasets and resolve company names across them. See our technical deep dive for details.
• Reproducibility: Every query returns deterministic results from a known data source, unlike general-purpose AI which may hallucinate regulatory facts.

2. Getting Started: API Key and Setup

Before connecting any AI client, you need an FDA Data MCP API key.

1. 1Sign up at regdatalab.com/signup. You'll receive an API key starting with fda_.
2. 2Free tier: Every new account starts with 1,000 free credits. Each tool call costs 1 credit. No credit card required.
3. 3Choose your client: FDA Data MCP works with Claude Desktop, Claude Code, Cursor, Windsurf, ChatGPT, and any MCP-compatible client.

The MCP endpoint is: https://www.regdatalab.com/mcp

Authentication uses a Bearer token in the Authorization header. All setup methods below include this configuration.

3. Connecting to Claude Desktop

Claude Desktop has built-in MCP support. There are two ways to connect:

Option 1: Integrations UI (recommended)

1. Open Claude Desktop → Settings (gear icon)
2. Navigate to Integrations → Add Integration
3. Name: FDA Data
4. URL: https://www.regdatalab.com/mcp
5. Add header: Authorization: Bearer YOUR_API_KEY

Option 2: JSON config file

Edit ~/Library/Application Support/Claude/claude_desktop_config.json on macOS (or the equivalent path on Windows/Linux):

{
  "mcpServers": {
    "fda-data": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-remote",
        "https://www.regdatalab.com/mcp",
        "--header",
        "Authorization: Bearer YOUR_API_KEY"
      ]
    }
  }
}

Replace YOUR_API_KEY with your actual API key. Restart Claude Desktop after saving. This method requires Node.js installed on your machine — mcp-remote bridges Claude Desktop's stdio transport to our HTTP endpoint.

4. Connecting to Claude Code

Claude Code (Anthropic's CLI coding agent) supports MCP natively with a single command:

claude mcp add fda-data https://www.regdatalab.com/mcp \
  --transport http \
  --header "Authorization: Bearer YOUR_API_KEY"

This adds FDA Data to your current project. To make it available across all projects:

claude mcp add fda-data https://www.regdatalab.com/mcp \
  --transport http \
  --scope user \
  --header "Authorization: Bearer YOUR_API_KEY"

Verify the connection by typing /mcp inside Claude Code. You should see fda-data listed with 23 tools.

5. Connecting to Cursor and Windsurf

Cursor

Add to ~/.cursor/mcp.json:

{
  "mcpServers": {
    "fda-data": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-remote",
        "https://www.regdatalab.com/mcp",
        "--header",
        "Authorization: Bearer YOUR_API_KEY"
      ]
    }
  }
}

Windsurf

Add to ~/.codeium/windsurf/mcp_config.json:

{
  "mcpServers": {
    "fda-data": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-remote",
        "https://www.regdatalab.com/mcp",
        "--header",
        "Authorization: Bearer YOUR_API_KEY"
      ]
    }
  }
}

Both Cursor and Windsurf use stdio transport, so mcp-remote bridges to our HTTP endpoint. Requires Node.js.

Team sharing with .mcp.json

For team projects, add a .mcp.json file to your repository root so teammates get FDA Data automatically:

{
  "mcpServers": {
    "fda-data": {
      "type": "http",
      "url": "https://www.regdatalab.com/mcp",
      "headers": {
        "Authorization": "Bearer ${FDA_DATA_API_KEY}"
      }
    }
  }
}

Each team member sets FDA_DATA_API_KEY in their environment. The file is safe to commit — no secrets are embedded.

6. Available Tools Overview

FDA Data MCP provides 23 tools organized by domain. Here's what you can query:

For the full tool reference with all parameters, see the documentation.

7. Example Queries and Workflows

Once connected, you can ask your AI agent natural language questions. The agent will automatically select the right tool and parameters. Here are real examples:

Company research

Prompt: "Look up Johnson & Johnson's FDA presence — facilities, recalls, and any warning letters."

The AI will call fda_lookup_company to find the company and its subsidiaries, then fda_search_recalls and fda_search_compliance to pull enforcement and compliance data. It will synthesize the results into a coherent summary, noting that J&J's subsidiaries (Janssen, Ethicon, DePuy) appear across different datasets.

Recall investigation

Prompt: "Find all Class I medical device recalls from the past year. Which companies have the most?"

The AI calls fda_search_recalls with classification and date filters, then analyzes the results to provide a ranked summary. It might note patterns like specific facilities or product types driving recall clusters.

Competitive intelligence

Prompt: "Compare 510(k) clearance activity between Medtronic and Abbott over the last 3 years. Which product categories are each company focusing on?"

The AI runs fda_search_510k for each company, then compares volumes, product codes, and trends. This type of multi-step analysis is where AI + structured data shines compared to manual database queries.

Facility due diligence

Prompt: "I'm evaluating a contract manufacturer called XYZ Pharma. Check their FDA registration status, inspection history, and any enforcement actions."

The AI cross-references facility registrations, inspection outcomes, and recall/compliance data to give you a comprehensive due diligence profile. Explore facility and inspection data on our inspection statistics and compliance dashboards.

8. Advanced Multi-Tool Workflows

The real power of MCP emerges when the AI chains multiple tools together in a single conversation. Here are patterns used by regulatory professionals and developers:

Supply chain risk assessment

Prompt: "I need a supply chain risk assessment for our CMO partners.
Here are the company names: [Company A, Company B, Company C].
For each, check:
1. FDA registration status
2. Inspection outcomes (OAI/VAI/NAI) in the last 3 years
3. Any recalls or warning letters
4. Overall risk rating (high/medium/low)"

The AI will systematically query each company across multiple tools, then synthesize a risk matrix. This workflow that might take hours manually can be completed in minutes.

Regulatory landscape analysis

Prompt: "What does the regulatory landscape look like for AI-enabled
radiology devices? Show me recent 510(k) clearances, any recalls,
and the competitive landscape."

The AI filters 510(k) data by relevant product codes, checks for recall activity in the same space, and identifies key players — all from a single conversational prompt.

Post-market surveillance

Prompt: "Monitor our competitor Acme Medical. Show me everything
the FDA has on them: facilities, clearances, recalls, inspections,
and compliance actions. Flag anything concerning."

This comprehensive lookup uses virtually every tool in the toolkit to build a complete regulatory profile. The AI's ability to flag patterns (e.g., "three OAI inspections at the same facility in two years") adds analytical value beyond raw data retrieval.

9. Building a Compliance Monitoring Workflow

Beyond one-off queries, FDA Data MCP can power automated compliance monitoring. Here's a pattern for building ongoing regulatory intelligence:

Weekly recall monitoring script

You can integrate FDA Data MCP into automated workflows using the raw HTTP endpoint:

curl -X POST https://www.regdatalab.com/mcp \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -H "Mcp-Session-Id: YOUR_SESSION_ID" \
  -d '{
    "jsonrpc": "2.0",
    "id": 1,
    "method": "tools/call",
    "params": {
      "name": "fda_search_recalls",
      "arguments": {
        "days_back": 7,
        "classification": "Class I"
      }
    }
  }'

Combine this with a cron job or CI/CD pipeline to get weekly alerts on new Class I recalls. Feed the results into Slack, email, or your compliance management system.

Integration with existing tools

FDA Data MCP works with any MCP-compatible client. Developers are integrating it into:

• Regulatory intelligence dashboards that combine FDA data with EU MDR, Health Canada, and TGA data
• Quality management systems that automatically cross-reference supplier FDA status during audits
• Due diligence workflows that pull FDA profiles for M&A target companies
• Investor research tools that flag FDA enforcement actions against portfolio companies

10. Tips for Effective FDA Data Queries

To get the best results from AI + FDA Data MCP:

• Be specific about companies: Use the full company name rather than abbreviations. "Becton Dickinson" works better than "BD". The normalization layer will handle subsidiaries, but starting with a clear name improves initial matching.
• Specify date ranges: When asking about trends, include the time period you care about. "Recalls in the last 2 years" is more useful than "recent recalls."
• Use classifications: Filter by recall class, inspection outcome (OAI/VAI/NAI), or 510(k) decision code to focus your results.
• Ask follow-up questions: MCP conversations are stateful within a session. After an initial lookup, you can drill deeper — "Show me the Class I recalls from that list" or "Which facilities had OAI outcomes?"
• Combine data sources: The most powerful queries cross-reference multiple datasets. Ask the AI to connect facility registrations with inspection outcomes and enforcement actions for a complete picture.
• Check the live dashboards: Use our statistics pages for visual exploration: recalls, 510(k), inspections, and compliance.

Further Reading

• FDA Data MCP Documentation — full tool reference and setup guides
• Live FDA Recall Statistics — interactive recall dashboard
• Live 510(k) Clearance Statistics — clearance trends and analysis
• Live FDA Inspection Statistics — inspection outcomes
• Live FDA Compliance Statistics — warning letters and citations
• FDA Drug & Device Recall Trends: What the Data Shows
• 510(k) Clearance Statistics: Medical Device Approval Trends
• How FDA Data MCP Works — technical deep dive