diff --git a/5-Applications/scripts/mcp_server.py b/5-Applications/scripts/mcp_server.py
deleted file mode 100644
index d3037cfa..00000000
--- a/5-Applications/scripts/mcp_server.py
+++ /dev/null
@@ -1,163 +0,0 @@
-#!/usr/bin/env python3
-"""
-Advanced MCP Server for Sovereign Research Stack
-
-Provides high-trust tools for:
-- Swarm Intelligence (Active Questioning)
-- Lean 4 Formal Verification (Consistentcy Checks)
-- Manifold Topology Discovery (PIST Substrate)
-- Academic Paper Context Extraction
-"""
-
-import sys
-import json
-import asyncio
-import subprocess
-from pathlib import Path
-from typing import Any, Dict, List, Optional
-
-# Add parent directory to path for imports
-sys.path.insert(0, str(Path(__file__).parent))
-sys.path.insert(0, str(Path(__file__).parent / "scripts"))
-
-try:
-    from mcp.server import Server
-    from mcp.server.stdio import stdio_server
-    from mcp.types import Tool, TextContent
-except ImportError:
-    print("MCP SDK not installed. Install with: pip install mcp")
-    sys.exit(1)
-
-# Import swarm components
-try:
-    from scripts.enhanced_integrated_swarm import (
-        EnhancedIntegratedSwarm,
-        create_demo_topology,
-        MathDatabase
-    )
-except ImportError:
-    print("Could not import swarm components")
-    sys.exit(1)
-
-# Global swarm instance
-_swarm_instance: Optional[EnhancedIntegratedSwarm] = None
-
-def get_swarm() -> EnhancedIntegratedSwarm:
-    """Get or create swarm instance"""
-    global _swarm_instance
-    if _swarm_instance is None:
-        topology = create_demo_topology()
-        math_db = MathDatabase()
-        _swarm_instance = EnhancedIntegratedSwarm(
-            topology=topology,
-            math_db=math_db,
-            num_agents=50
-        )
-    return _swarm_instance
-
-# Create MCP server
-server = Server("sovereign-research-stack")
-
-@server.list_tools()
-async def list_tools() -> List[Tool]:
-    """List available advanced tools"""
-    return [
-        Tool(
-            name="ask_swarm",
-            description="Consult the 50-agent swarm on complex reasoning tasks.",
-            inputSchema={
-                "type": "object",
-                "properties": {
-                    "question": {"type": "string"},
-                    "context": {"type": "string"},
-                    "domain": {"type": "string", "default": "theoretical_physics"}
-                },
-                "required": ["question"]
-            }
-        ),
-        Tool(
-            name="query_manifold_topology",
-            description="Returns the current PIST-based virtual substrate state (Mass Field, Resonance).",
-            inputSchema={"type": "object", "properties": {}}
-        ),
-        Tool(
-            name="verify_lean_consistency",
-            description="Runs diagnostics on Lean modules to check for structural integrity.",
-            inputSchema={
-                "type": "object",
-                "properties": {
-                    "module": {"type": "string", "description": "Basename of the .lean file"}
-                },
-                "required": ["module"]
-            }
-        ),
-        Tool(
-            name="get_academic_validation",
-            description="Retrieves arXiv-backed validation points for Semantic RG structures.",
-            inputSchema={"type": "object", "properties": {}}
-        ),
-        Tool(
-            name="teach_swarm_academic_papers",
-            description="Broadcasts academic paper content to the 50-agent swarm to update their research context.",
-            inputSchema={"type": "object", "properties": {}}
-        )
-    ]
-
-@server.call_tool()
-async def call_tool(name: str, arguments: Dict[str, Any]) -> List[TextContent]:
-    """Execute tool logic"""
-    swarm = get_swarm()
-    
-    if name == "ask_swarm":
-        response = swarm.research_api.ask_question(
-            question=arguments["question"],
-            context=arguments.get("context", ""),
-            domain=arguments.get("domain", "theoretical_physics")
-        )
-        return [TextContent(type="text", text=f"Swarm Consensus:\n{response}")]
-
-    elif name == "query_manifold_topology":
-        summary = swarm.optimizer.get_optimization_summary()
-        return [TextContent(type="text", text=json.dumps(summary, indent=2))]
-
-    elif name == "verify_lean_consistency":
-        module = arguments["module"]
-        lean_path = Path("/home/allaun/Documents/Research Stack/0-Core-Formalism/lean/Semantics/Semantics") / module
-        if not lean_path.suffix == ".lean":
-            lean_path = lean_path.with_suffix(".lean")
-            
-        if lean_path.exists():
-            # Perform pseudo-verification since we don't want to run full 'lake build' in a quick tool
-            with open(lean_path) as f:
-                content = f.read()
-            defs = content.count("def ")
-            theorems = content.count("theorem ")
-            sorries = content.count("sorry")
-            status = "Verified (Placeholder Sorries Exist)" if sorries > 0 else "Fully Proved"
-            return [TextContent(type="text", text=f"Module: {module}\nDefinitions: {defs}\nTheorems: {theorems}\nStatus: {status}")]
-        return [TextContent(type="text", text=f"Error: {module} not found.")]
-
-    elif name == "get_academic_validation":
-        papers_file = Path("/home/allaun/Documents/Research Stack/shared-data/data/academic_papers_validation.json")
-        if papers_file.exists():
-            with open(papers_file) as f:
-                data = json.load(f)
-            return [TextContent(type="text", text=json.dumps(data, indent=2))]
-    
-    elif name == "teach_swarm_academic_papers":
-        script_path = Path("/home/allaun/Documents/Research Stack/scripts/teach_swarm_academic_papers.py")
-        result = subprocess.run([sys.executable, str(script_path)], capture_output=True, text=True)
-        if result.returncode == 0:
-            return [TextContent(type="text", text=f"Swarm Teaching Successful:\n{result.stdout[-1000:]}")]
-        return [TextContent(type="text", text=f"Error teaching swarm:\n{result.stderr}")]
-        return [TextContent(type="text", text="Error: Validation data missing. Run teach_swarm script first.")]
-
-    else:
-        raise ValueError(f"Unknown tool: {name}")
-
-async def main():
-    async with stdio_server() as (read_stream, write_stream):
-        await server.run(read_stream, write_stream, server.create_initialization_options())
-
-if __name__ == "__main__":
-    asyncio.run(main())