chore(pending): move python MCP server into pending quarantine

pending/lean_unification/5-Applications/scripts/mcp_server.py

@@ -0,0 +1,163 @@
+#!/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())