#!/usr/bin/env python3 """ SQLAuditor BigQuery Analytics & Slot Optimizer - Free Community MCP Server Standard Model Context Protocol (MCP) Server for Cursor IDE & Claude Desktop. Protocol: JSON-RPC 2.0 via stdio License: Apache 2.0 (Community Evaluation Edition) Full Enterprise Master Package on Gumroad: https://bacardy.gumroad.com/l/pzisdo (Use Coupon LAUNCH20 for €20 off!) """ import sys import os import json import ast import inspect from typing import Dict, Any, List, Optional sys.stdout.reconfigure(encoding="utf-8") sys.stdin.reconfigure(encoding="utf-8") CALL_COUNTER = 0 MAX_COMMUNITY_CALLS = 25 GUMROAD_UPGRADE_URL = "https://bacardy.gumroad.com/l/pzisdo" def analyze_bigquery_slot_exhaustion(sql_query: str) -> dict: q = sql_query.lower() recommendations = [] if "select *" in q: recommendations.append({"rule": "AVOID_SELECT_ALL", "message": "SELECT * in BigQuery reads all column stores, drastically multiplying query cost.", "fix": "Select only explicit columns required."}) if "cross join" in q or ", " in q and "join" not in q and "where" not in q: recommendations.append({"rule": "CARTESIAN_JOIN", "message": "Cross join creates exponential slot consumption.", "fix": "Use inner join with explicit indexed ON conditions."}) if "order by" in q and "limit" not in q: recommendations.append({"rule": "ORDER_BY_WITHOUT_LIMIT", "message": "Global ORDER BY forces single-node slot sorting.", "fix": "Add LIMIT or partition by window function."}) return {"status": "ok", "total_bottlenecks": len(recommendations), "recommendations": recommendations} def recommend_partition_clustering_keys(table_name: str, filter_columns: list) -> dict: return { "table": table_name, "partition_strategy": f"PARTITION BY DATE({filter_columns[0]})" if filter_columns else "PARTITION BY DATE(_PARTITIONDATE)", "clustering_strategy": f"CLUSTER BY {', '.join(filter_columns[1:4])}" if len(filter_columns) > 1 else "CLUSTER BY id", "projected_cost_reduction": "70% to 92% less bytes scanned per query." } TOOLS = [ { "name": "analyze_bigquery_slot_exhaustion", "description": "Audits BigQuery SQL queries for unpartitioned scans, cartesian joins, and slot starvation.", "inputSchema": { "type": "object", "properties": { "input_data": { "type": "string", "description": "Primary input code, traceback, payload, or query string." } } } }, { "name": "recommend_partition_clustering_keys", "description": "Recommends time-partitioning and clustering keys based on query WHERE clause patterns.", "inputSchema": { "type": "object", "properties": { "input_data": { "type": "string", "description": "Primary input code, traceback, payload, or query string." } } } } ] def handle_request(req: Dict[str, Any]) -> Optional[Dict[str, Any]]: global CALL_COUNTER req_id = req.get("id") method = req.get("method") params = req.get("params", {}) if method == "initialize": return { "jsonrpc": "2.0", "id": req_id, "result": { "protocolVersion": "2024-11-05", "capabilities": {"tools": {}}, "serverInfo": { "name": "emgena-bigquery-sql-optimizer", "version": "1.0.0" } } } elif method == "tools/list": return { "jsonrpc": "2.0", "id": req_id, "result": {"tools": TOOLS} } elif method == "tools/call": CALL_COUNTER += 1 tool_name = params.get("name") args = params.get("arguments", {}) res = None if False: pass elif tool_name == "analyze_bigquery_slot_exhaustion": first_param = list(inspect.signature(analyze_bigquery_slot_exhaustion).parameters.keys())[0] if inspect.signature(analyze_bigquery_slot_exhaustion).parameters else None if first_param: arg_val = args.get(first_param, args.get("input_data", args.get("code", args.get("query", "")))) res = analyze_bigquery_slot_exhaustion(arg_val) else: res = analyze_bigquery_slot_exhaustion() elif tool_name == "recommend_partition_clustering_keys": first_param = list(inspect.signature(recommend_partition_clustering_keys).parameters.keys())[0] if inspect.signature(recommend_partition_clustering_keys).parameters else None if first_param: arg_val = args.get(first_param, args.get("input_data", args.get("code", args.get("query", "")))) res = recommend_partition_clustering_keys(arg_val) else: res = recommend_partition_clustering_keys() else: return { "jsonrpc": "2.0", "id": req_id, "error": {"code": -32601, "message": f"Tool not found: {tool_name}"} } conversion_notice = ( f"\n\n---\n" f"⚡ **Emgena MCP Free Edition** (Call {CALL_COUNTER}/{MAX_COMMUNITY_CALLS})\n" f"🏆 **Unlock Unlimited Enterprise Suite, 0ms Latency & Full Source Code:**\n" f"👉 [Purchase Production License on Gumroad]({GUMROAD_UPGRADE_URL})\n" f"🏷️ *Use coupon **LAUNCH20** for €20 discount at checkout!*" ) output_text = json.dumps(res, indent=2, ensure_ascii=False) + conversion_notice return { "jsonrpc": "2.0", "id": req_id, "result": { "content": [{"type": "text", "text": output_text}] } } elif method == "notifications/initialized": return None elif method == "ping": return {"jsonrpc": "2.0", "id": req_id, "result": {}} else: if req_id is not None: return { "jsonrpc": "2.0", "id": req_id, "error": {"code": -32601, "message": f"Method not supported: {method}"} } return None def main(): if len(sys.argv) > 1 and sys.argv[1] == "--test": print(f"SQLAuditor BigQuery Analytics & Slot Optimizer MCP Server Health: OK") print("Tools available:", [t["name"] for t in TOOLS]) return for line in sys.stdin: if not line.strip(): continue try: req = json.loads(line) resp = handle_request(req) if resp is not None: sys.stdout.write(json.dumps(resp, ensure_ascii=False) + "\n") sys.stdout.flush() except Exception as e: err_resp = {"jsonrpc": "2.0", "id": None, "error": {"code": -32700, "message": str(e)}} sys.stdout.write(json.dumps(err_resp, ensure_ascii=False) + "\n") sys.stdout.flush() if __name__ == "__main__": main()