MCP Local WP MCP server

MCP Server Local WP

🎯 What if your AI assistant could actually SEE your WordPress database?

A Model Context Protocol (MCP) server that gives AI assistants like Claude and Cursor direct, read-only access to your Local by Flywheel WordPress database. No more guessing table structures. No more writing SQL queries blind. Your AI can now understand your actual data.

🤔 What's an MCP Server?

Think of MCP (Model Context Protocol) as a secure bridge between AI assistants and your development tools. Instead of copying and pasting database schemas or query results, MCP servers let AI assistants directly interact with your tools while you maintain complete control.

Without MCP: "Hey AI, I think there's a table called wpsomething with a column that might be named usermeta... can you write a query?" With MCP: "Hey AI, check what's in my database and write the exact query I need."

💡 The WordPress Developer's Dilemma

Picture this: You're debugging a LearnDash integration issue. Quiz results aren't syncing properly. You fire up Cursor to help diagnose the problem, but without database access, even the most advanced AI is just making educated guesses about your table structures.

The Real-World Impact

Here's an actual support ticket we were working on. The task was simple: fetch quiz activity data from LearnDash tables.

❌ Before MCP Server (AI Flying Blind):

The AI tried its best, suggesting this query:

$quiz_activities = $wpdb->get_results( 
  $wpdb->prepare( 
    'SELECT post_id, activity_meta FROM ' . esc_sql( LDLMS_DB::get_table_name( 'user_activity' ) ) . ' 
     WHERE user_id=%d AND activity_type=%s AND activity_status=1 AND activity_completed IS NOT NULL',
    $user_id, 
    'quiz' 
  ), 
  ARRAY_A 
);

Problem? The activity_meta column doesn't exist! LearnDash stores metadata in a completely separate table with a different structure. Without database access, the AI made reasonable but incorrect assumptions. You'd spend the next 20 minutes manually correcting table names, discovering relationships, and rewriting the query.

✅ After MCP Server (AI With X-Ray Vision):

With database access, the AI immediately saw the actual table structure and wrote:

$quiz_activities = $wpdb->get_results(
  $wpdb->prepare(
    'SELECT ua.post_id, ua.activity_id, uam.activity_meta_key, uam.activity_meta_value 
     FROM ' . esc_sql( LDLMS_DB::get_table_name( 'user_activity' ) ) . ' ua
     LEFT JOIN ' . esc_sql( LDLMS_DB::get_table_name( 'user_activity_meta' ) ) . ' uam 
     ON ua.activity_id = uam.activity_id 
     WHERE ua.user_id=%d AND ua.activity_type=%s AND ua.activity_completed IS NOT NULL
     AND uam.activity_meta_key IN (%s, %s, %s)',
    $user_id,
    'quiz',
    'percentage',
    'points',
    'total_points'
  ),
  ARRAY_A
);

The difference? The AI could see that metadata lives in a separate useractivitymeta table, understood the relationship through activity_id, and knew exactly which meta keys were available. First try. Zero guesswork. Problem solved.

🚀 Why This Changes Everything

When your AI assistant can read your database:

• No more schema guessing - It sees your actual tables and columns
• Accurate JOIN operations - It understands table relationships
• Real data validation - It can verify that data exists before suggesting queries
• Plugin-aware development - It adapts to any plugin's custom tables (WooCommerce, LearnDash, etc.)
• Instant debugging - "Show me all users who haven't completed quiz ID 42" becomes a 5-second task

Shortened. The full README is on GitHub.