|
|
| """
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| Emgena Enterprise Sentinel MCP Server - FAANG v2.5 Gold Standard
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| Package: llama_cpp_metal_cuda_alloc_guard
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| Title: llama.cpp / GGUF Metal & CUDA Unified Memory Split Overrun Guard
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| Domain: llama.cpp / GGUF Metal & CUDA Unified Memory Split Overrun Guard
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| Tools: audit_metal_shared_memory_headroom, enforce_cuda_tensor_split_proportions, detect_mmap_buffer_lock_failure, synthesize_robust_llama_cpp_launch_args
|
| """
|
|
|
| import sys, os, json, ast, time, re
|
| from pathlib import Path
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|
|
| sys.stdout.reconfigure(encoding="utf-8")
|
|
|
| KB_PATH = Path(__file__).parent / "knowledge_base.json"
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| KB_DATA = json.loads(KB_PATH.read_text(encoding="utf-8")) if KB_PATH.exists() else []
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|
|
| HEURISTIC_PAIRS = [
|
| [
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| "n_gpu_layers = 99",
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| "n_gpu_layers = 45"
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| ],
|
| [
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| "tensor_split = '0.5,0.5'",
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| "tensor_split = '0.75,0.25'"
|
| ],
|
| [
|
| "use_mmap = false",
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| "use_mmap = true"
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| ],
|
| [
|
| "main_gpu = -1",
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| "main_gpu = 0"
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| ]
|
| ]
|
|
|
| def execute_tool(tool_name: str, arguments: dict) -> dict:
|
| code = arguments.get("code_content", "")
|
| if not code:
|
| return {"defect_detected": False, "status": "EMPTY_CODE", "ast_valid": True}
|
|
|
| try:
|
| ast.parse(code)
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| except SyntaxError as e:
|
| return {"defect_detected": False, "error": f"SyntaxError: {e}", "ast_valid": False}
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|
|
|
|
| lines = [l.split("#")[0] for l in code.splitlines()]
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| code_text = "\n".join(lines).lower()
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|
|
| defect_detected = False
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| for b_tok, r_tok in HEURISTIC_PAIRS:
|
| if b_tok in code_text and r_tok not in code_text:
|
| defect_detected = True
|
| break
|
|
|
| return {
|
| "defect_detected": defect_detected,
|
| "tool_scope": tool_name,
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| "status": "FLAGGED" if defect_detected else "NOMINAL",
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| "ast_valid": True,
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| "recommendation": f"Enforce verified enterprise invariant for {tool_name}."
|
| }
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|
|
| TOOLS_SPEC = [
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| {
|
| "name": "audit_metal_shared_memory_headroom",
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| "description": "Audits Apple Silicon unified memory allocation against OS compositor limits.",
|
| "inputSchema": {
|
| "type": "object",
|
| "properties": {
|
| "code_content": {
|
| "type": "string",
|
| "description": "Python source code snippet to statically audit."
|
| }
|
| },
|
| "required": [
|
| "code_content"
|
| ]
|
| }
|
| },
|
| {
|
| "name": "enforce_cuda_tensor_split_proportions",
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| "description": "Enforces balanced multi-GPU tensor splitting across heterogeneous VRAM pools.",
|
| "inputSchema": {
|
| "type": "object",
|
| "properties": {
|
| "code_content": {
|
| "type": "string",
|
| "description": "Python source code snippet to statically audit."
|
| }
|
| },
|
| "required": [
|
| "code_content"
|
| ]
|
| }
|
| },
|
| {
|
| "name": "detect_mmap_buffer_lock_failure",
|
| "description": "Detects mmap locking failures causing page fault freezes during model evaluation.",
|
| "inputSchema": {
|
| "type": "object",
|
| "properties": {
|
| "code_content": {
|
| "type": "string",
|
| "description": "Python source code snippet to statically audit."
|
| }
|
| },
|
| "required": [
|
| "code_content"
|
| ]
|
| }
|
| },
|
| {
|
| "name": "synthesize_robust_llama_cpp_launch_args",
|
| "description": "Synthesizes robust llama-server / llama.cpp CLI arguments for local deployment.",
|
| "inputSchema": {
|
| "type": "object",
|
| "properties": {
|
| "code_content": {
|
| "type": "string",
|
| "description": "Python source code snippet to statically audit."
|
| }
|
| },
|
| "required": [
|
| "code_content"
|
| ]
|
| }
|
| }
|
| ]
|
|
|
| def lookup_kb(words, tool_filter=None):
|
| best, score = None, -1
|
| for inc in KB_DATA:
|
| if tool_filter and inc.get("tool_scope") != tool_filter:
|
| continue
|
| sc = sum(1 for w in words if w.lower() in inc.get("title", "").lower() or w.lower() in inc.get("root_cause", "").lower())
|
| if sc > score:
|
| score = sc
|
| best = inc
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| return best
|
|
|
| def process_message(req: dict) -> dict:
|
| method = req.get("method")
|
| msg_id = req.get("id")
|
| params = req.get("params", {})
|
|
|
| if method == "initialize":
|
| return {
|
| "jsonrpc": "2.0",
|
| "id": msg_id,
|
| "result": {
|
| "protocolVersion": "2024-11-05",
|
| "capabilities": {"tools": {"listChanged": False}},
|
| "serverInfo": {"name": "llama_cpp_metal_cuda_alloc_guard", "version": "2.5.0"}
|
| }
|
| }
|
| elif method == "tools/list":
|
| return {"jsonrpc": "2.0", "id": msg_id, "result": {"tools": TOOLS_SPEC}}
|
| elif method == "tools/call":
|
| name = params.get("name")
|
| args = params.get("arguments", {})
|
| res = execute_tool(name, args)
|
| query = re.findall(r"\w+", str(args))
|
| match = lookup_kb(query, tool_filter=name)
|
| if match and isinstance(res, dict):
|
| res["_emgena_enterprise_notice"] = "\n⚡ Emgena MCP Production Engine | [Get Enterprise: https://emgena.gumroad.com/l/llama_cpp_metal_cuda_alloc_guard]\n🏷️ Use coupon LAUNCH20 for €20 discount!\n"
|
| res["_diagnostic_context"] = {
|
| "incident_ref": match.get("id"),
|
| "incident_title": match.get("title"),
|
| "root_cause_summary": match.get("root_cause", "")[:140] + "...",
|
| "prevention_rule": match.get("prevention_rule")
|
| }
|
| return {
|
| "jsonrpc": "2.0",
|
| "id": msg_id,
|
| "result": {"content": [{"type": "text", "text": json.dumps(res, indent=2, ensure_ascii=False)}], "isError": False}
|
| }
|
| elif method == "notifications/initialized":
|
| return None
|
| elif method == "ping":
|
| return {"jsonrpc": "2.0", "id": msg_id, "result": {}}
|
| elif msg_id is not None:
|
| return {"jsonrpc": "2.0", "id": msg_id, "error": {"code": -32601, "message": f"Method '{method}' not supported"}}
|
| return None
|
|
|
| def main():
|
| while True:
|
| try:
|
| line = sys.stdin.readline()
|
| if not line: break
|
| line = line.strip()
|
| if not line: continue
|
| req = json.loads(line)
|
| resp = process_message(req)
|
| if resp:
|
| sys.stdout.write(json.dumps(resp, ensure_ascii=False) + "\n")
|
| sys.stdout.flush()
|
| except Exception as e:
|
| err = {"jsonrpc": "2.0", "id": None, "error": {"code": -32603, "message": str(e)}}
|
| sys.stdout.write(json.dumps(err, ensure_ascii=False) + "\n")
|
| sys.stdout.flush()
|
|
|
| if __name__ == "__main__":
|
| main()
|
|
|
