Datasets:

emgena
/

emgena_dataops_query_performance_mcp_teaser

#!/usr/bin/env python3
"""
Emgena DataOps & Query Performance MCP Guard (Enterprise Edition)
Standard Model Context Protocol (MCP) Server for Cursor IDE & Claude Desktop.
Protocol: JSON-RPC 2.0 via stdio
Zero external dependencies: 100% Python standard library.
"""

import sys
import os
import json
import ast
import re
from pathlib import Path
from typing import Dict, Any, List, Optional

sys.stdout.reconfigure(encoding="utf-8")
sys.stdin.reconfigure(encoding="utf-8")

KB_FILE = Path(__file__).parent / "knowledge_base.json"
KNOWLEDGE_BASE = []
if KB_FILE.exists():
    try:
        KNOWLEDGE_BASE = json.loads(KB_FILE.read_text(encoding="utf-8"))
    except Exception:
        pass

# =====================================================================
# CORE DATAOPS & QUERY PERFORMANCE LOGIC
# =====================================================================

def diagnose_slow_query(query_or_plan: str, target_engine: str = "postgresql") -> Dict[str, Any]:
    """Analyzes SQL queries and PostgreSQL EXPLAIN (ANALYZE, BUFFERS) execution plans, diagnosing sequential scans and proposing concurrent indexes."""
    issues = []
    proposed_indexes = []

    # 1. Sequential Scan on large tables
    seq_match = re.search(r"Seq Scan on (\w+).*?rows=(\d+)", query_or_plan, re.IGNORECASE)
    if seq_match or ("seq scan" in query_or_plan.lower()):
        tbl = seq_match.group(1) if seq_match else "target_table"
        rows = seq_match.group(2) if seq_match else "high"
        issues.append({
            "severity": "HIGH",
            "type": "FULL_TABLE_SEQ_SCAN",
            "table": tbl,
            "message": f"Full table sequential scan detected on table '{tbl}' (estimated rows: {rows}). High I/O and shared buffer churn.",
            "optimization": f"Add a composite B-Tree index on filtered/joined predicate columns."
        })
        proposed_indexes.append(f"CREATE INDEX CONCURRENTLY idx_{tbl}_query_perf ON {tbl} (created_at DESC, status);")

    # 2. SELECT * on wide tables
    if re.search(r"SELECT\s+\*\s+FROM", query_or_plan, re.IGNORECASE):
        issues.append({
            "severity": "MEDIUM",
            "type": "SELECT_STAR_OVERFETCHING",
            "message": "Wildcard SELECT * forces PostgreSQL to read all toasted columns, bypassing index-only scans.",
            "optimization": "Explicitly project only required columns to enable Index-Only Scans."
        })

    # 3. Missing LIMIT in high-volume query
    if ("select " in query_or_plan.lower()) and ("limit" not in query_or_plan.lower()) and ("count(" not in query_or_plan.lower()):
        issues.append({
            "severity": "HIGH",
            "type": "UNBOUNDED_RESULT_SET",
            "message": "Query lacks an explicit LIMIT clause, risking client out-of-memory under large result sets.",
            "optimization": "Add 'LIMIT 100' or keyset pagination ('WHERE id > :last_id ORDER BY id LIMIT 50')."
        })

    # 4. Hash Join with high buffer read
    if "hash join" in query_or_plan.lower() and "buffers: shared read" in query_or_plan.lower():
        issues.append({
            "severity": "HIGH",
            "type": "DISK_BUFFER_SPILL_JOIN",
            "message": "Hash Join spilled to disk or read from cold shared buffers due to insufficient work_mem.",
            "optimization": "Increase work_mem for the session: 'SET LOCAL work_mem = '64MB';'."
        })

    score = max(0, 100 - (len(issues) * 25))
    return {
        "engine": target_engine,
        "performance_score": score,
        "status": "OPTIMAL" if score == 100 else "DEGRADED" if score >= 60 else "CRITICAL",
        "total_issues": len(issues),
        "issues": issues,
        "proposed_indexes": proposed_indexes
    }


def guard_ddl_migrations(migration_code: str) -> Dict[str, Any]:
    """Scans Alembic and SQL schema migration scripts for dangerous AccessExclusiveLock timeouts, table rewrites, and non-concurrent indexes."""
    findings = []

    # 1. Missing lock_timeout
    if ("op.alter_column" in migration_code or "op.add_column" in migration_code or "op.drop_column" in migration_code) and \
       "lock_timeout" not in migration_code:
        findings.append({
            "severity": "CRITICAL",
            "rule": "MISSING_MIGRATION_LOCK_TIMEOUT",
            "message": "ALTER TABLE statement without SET LOCAL lock_timeout can queue behind slow queries and cause a total connection pool stall.",
            "remediation": "Prepend migration with: op.execute(\"SET LOCAL lock_timeout = '2s';\")"
        })

    # 2. Non-concurrent index creation
    if "op.create_index" in migration_code and "postgresql_concurrently=True" not in migration_code:
        findings.append({
            "severity": "HIGH",
            "rule": "NON_CONCURRENT_INDEX_CREATION",
            "message": "Creating an index without 'postgresql_concurrently=True' holds a SHARE lock, blocking all concurrent writes.",
            "remediation": "Enable autocommit block and specify 'postgresql_concurrently=True' in op.create_index."
        })

    # 3. Adding NOT NULL column without server default
    if "nullable=False" in migration_code and "server_default=" not in migration_code and "op.add_column" in migration_code:
        findings.append({
            "severity": "HIGH",
            "rule": "ADD_NOT_NULL_WITHOUT_DEFAULT",
            "message": "Adding a NOT NULL column without server_default fails on existing non-empty tables and locks writes.",
            "remediation": "Add column as nullable first, backfill data in batches, then alter column to set NOT NULL."
        })

    score = max(0, 100 - (len(findings) * 30))
    return {
        "audit_type": "DDL_MIGRATION_SAFETY",
        "safety_score": score,
        "status": "APPROVED" if len(findings) == 0 else "REJECTED_DANGEROUS_LOCKS",
        "total_findings": len(findings),
        "findings": findings
    }


def audit_connection_pool(config_or_code: str) -> Dict[str, Any]:
    """Audits SQLAlchemy Async, asyncpg, and pgBouncer configurations for unclosed sessions, pool exhaustion, and greenlet worker leaks."""
    leaks = []

    # 1. Missing AsyncSession context manager
    if "session = AsyncSession(" in config_or_code and "async with" not in config_or_code:
        leaks.append({
            "severity": "CRITICAL",
            "type": "UNCLOSED_ASYNC_SESSION",
            "message": "AsyncSession initialized without 'async with' context manager. Connections remain checked out of the pool indefinitely.",
            "fix": "Wrap database interactions inside: 'async with AsyncSessionLocal() as session:'."
        })

    # 2. Zero pool recycle / missing timeout
    if "create_async_engine" in config_or_code and "pool_recycle" not in config_or_code:
        leaks.append({
            "severity": "MEDIUM",
            "type": "MISSING_POOL_RECYCLE",
            "message": "create_async_engine lacks pool_recycle parameter. Dead or severed stateful connections cause transient 500 errors.",
            "fix": "Set pool_recycle=1800 (30 minutes) and pool_pre_ping=True."
        })

    # 3. Unbounded max_overflow
    if "max_overflow=0" not in config_or_code and "max_overflow" in config_or_code:
        m = re.search(r"max_overflow\s*=\s*(\d+)", config_or_code)
        if m and int(m.group(1)) > 50:
            leaks.append({
                "severity": "HIGH",
                "type": "EXCESSIVE_MAX_OVERFLOW",
                "message": f"max_overflow ({m.group(1)}) is dangerously high, risking PostgreSQL max_connections exhaustion.",
                "fix": "Bound max_overflow <= 10 and delegate connection multiplexing to pgBouncer in transaction mode."
            })

    score = max(0, 100 - (len(leaks) * 30))
    return {
        "audit_type": "CONNECTION_POOL_INTEGRITY",
        "pool_health_score": score,
        "status": "HEALTHY" if score >= 80 else "AT_RISK",
        "total_issues": len(leaks),
        "issues": leaks
    }


def cache_thundering_herd_guard(cache_code: str) -> Dict[str, Any]:
    """Audits Redis / Memcached caching patterns for cache stampede (thundering herd) risks and recommends probabilistic early expiration."""
    vulnerabilities = []

    # 1. Simple get-or-fetch without locking
    if ("redis.get(" in cache_code or "cache.get(" in cache_code) and \
       ("lock(" not in cache_code and "setnx" not in cache_code.lower() and "stale" not in cache_code.lower()):
        vulnerabilities.append({
            "severity": "HIGH",
            "risk": "CACHE_STAMPEDE_THUNDERING_HERD",
            "message": "Cache miss triggers direct database query without distributed locking. Under 5,000 req/s, all concurrent requests hit the DB simultaneously on key expiry.",
            "remediation": "Implement distributed lock (redis.set(key, val, nx=True, ex=5)) or probabilistic early recomputation (XFetch algorithm)."
        })

    # 2. Static TTL without jitter
    if re.search(r"ex\s*=\s*3600|ttl\s*=\s*3600|expire\s*\(\s*key\s*,\s*\d+\s*\)", cache_code) and "random" not in cache_code:
        vulnerabilities.append({
            "severity": "MEDIUM",
            "risk": "SYNCHRONIZED_CACHE_EXPIRY",
            "message": "Static cache TTL without randomized jitter causes batch keys to expire at the exact same second.",
            "remediation": "Add jitter to expiration: ttl = base_ttl + random.randint(0, int(base_ttl * 0.1))."
        })

    score = max(0, 100 - (len(vulnerabilities) * 35))
    return {
        "audit_type": "CACHE_STAMPEDE_DEFENSE",
        "cache_resilience_score": score,
        "status": "PROTECTED" if score == 100 else "VULNERABLE_TO_STAMPEDE",
        "findings_count": len(vulnerabilities),
        "findings": vulnerabilities
    }


# =====================================================================
# STANDARD MCP JSON-RPC 2.0 PROTOCOL LOOP (STDIO)
# =====================================================================

TOOLS = [
    {
        "name": "diagnose_slow_query",
        "description": "Analyzes PostgreSQL EXPLAIN (ANALYZE, BUFFERS) plans and SQL queries, diagnosing sequential scans and proposing exact concurrent B-Tree/BRIN indexes.",
        "inputSchema": {
            "type": "object",
            "properties": {
                "query_or_plan": {"type": "string", "description": "SQL query or EXPLAIN (ANALYZE, BUFFERS) text to analyze."},
                "target_engine": {"type": "string", "description": "Database engine (postgresql, mysql, clickhouse)", "default": "postgresql"}
            },
            "required": ["query_or_plan"]
        }
    },
    {
        "name": "guard_ddl_migrations",
        "description": "Scans Alembic and SQL schema migration scripts for dangerous table lock patterns (missing lock_timeout, non-concurrent indexes, table rewrites).",
        "inputSchema": {
            "type": "object",
            "properties": {
                "migration_code": {"type": "string", "description": "Alembic Python migration script or raw DDL SQL."}
            },
            "required": ["migration_code"]
        }
    },
    {
        "name": "audit_connection_pool",
        "description": "Audits SQLAlchemy Async, asyncpg, and pgBouncer configs for unclosed sessions, connection pool exhaustion, and greenlet worker leaks.",
        "inputSchema": {
            "type": "object",
            "properties": {
                "config_or_code": {"type": "string", "description": "Database connection and session handling code snippet."}
            },
            "required": ["config_or_code"]
        }
    },
    {
        "name": "cache_thundering_herd_guard",
        "description": "Audits Redis / Memcached caching patterns for cache stampede (thundering herd) risks, missing distributed locks, and synchronized TTL expiry.",
        "inputSchema": {
            "type": "object",
            "properties": {
                "cache_code": {"type": "string", "description": "Application caching logic to inspect."}
            },
            "required": ["cache_code"]
        }
    }
]


def handle_request(req: Dict[str, Any]) -> Optional[Dict[str, Any]]:
    method = req.get("method")
    req_id = req.get("id")
    params = req.get("params", {})

    if method == "initialize":
        return {
            "jsonrpc": "2.0",
            "id": req_id,
            "result": {
                "protocolVersion": "2024-11-05",
                "capabilities": {"tools": {}},
                "serverInfo": {
                    "name": "emgena-dataops-query-performance",
                    "version": "2.0.0"
                }
            }
        }
    elif method == "tools/list":
        return {
            "jsonrpc": "2.0",
            "id": req_id,
            "result": {"tools": TOOLS}
        }
    elif method == "tools/call":
        tool_name = params.get("name")
        args = params.get("arguments", {})

        result_data = {}
        if tool_name == "diagnose_slow_query":
            result_data = diagnose_slow_query(args.get("query_or_plan", ""), args.get("target_engine", "postgresql"))
        elif tool_name == "guard_ddl_migrations":
            result_data = guard_ddl_migrations(args.get("migration_code", ""))
        elif tool_name == "audit_connection_pool":
            result_data = audit_connection_pool(args.get("config_or_code", ""))
        elif tool_name == "cache_thundering_herd_guard":
            result_data = cache_thundering_herd_guard(args.get("cache_code", ""))
        else:
            return {
                "jsonrpc": "2.0",
                "id": req_id,
                "error": {"code": -32601, "message": f"Tool not found: {tool_name}"}
            }

        return {
            "jsonrpc": "2.0",
            "id": req_id,
            "result": {
                "content": [{"type": "text", "text": json.dumps(result_data, indent=2, ensure_ascii=False)}]
            }
        }
    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("Emgena DataOps Query Performance MCP Server Health: OK")
        res = diagnose_slow_query("Seq Scan on orders (cost=0.00..1524.00 rows=50000 width=32)")
        print("Test Query Result:", res.get("status"))
        return

    # Standard MCP JSON-RPC line-by-line stdio loop
    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()

	#!/usr/bin/env python3
	"""
	Emgena DataOps & Query Performance MCP Guard (Enterprise Edition)
	Standard Model Context Protocol (MCP) Server for Cursor IDE & Claude Desktop.
	Protocol: JSON-RPC 2.0 via stdio
	Zero external dependencies: 100% Python standard library.
	"""

	import sys
	import os
	import json
	import ast
	import re
	from pathlib import Path
	from typing import Dict, Any, List, Optional

	sys.stdout.reconfigure(encoding="utf-8")
	sys.stdin.reconfigure(encoding="utf-8")

	KB_FILE = Path(__file__).parent / "knowledge_base.json"
	KNOWLEDGE_BASE = []
	if KB_FILE.exists():
	try:
	KNOWLEDGE_BASE = json.loads(KB_FILE.read_text(encoding="utf-8"))
	except Exception:
	pass

	# =====================================================================
	# CORE DATAOPS & QUERY PERFORMANCE LOGIC
	# =====================================================================

	def diagnose_slow_query(query_or_plan: str, target_engine: str = "postgresql") -> Dict[str, Any]:
	"""Analyzes SQL queries and PostgreSQL EXPLAIN (ANALYZE, BUFFERS) execution plans, diagnosing sequential scans and proposing concurrent indexes."""
	issues = []
	proposed_indexes = []

	# 1. Sequential Scan on large tables
	seq_match = re.search(r"Seq Scan on (\w+).*?rows=(\d+)", query_or_plan, re.IGNORECASE)
	if seq_match or ("seq scan" in query_or_plan.lower()):
	tbl = seq_match.group(1) if seq_match else "target_table"
	rows = seq_match.group(2) if seq_match else "high"
	issues.append({
	"severity": "HIGH",
	"type": "FULL_TABLE_SEQ_SCAN",
	"table": tbl,
	"message": f"Full table sequential scan detected on table '{tbl}' (estimated rows: {rows}). High I/O and shared buffer churn.",
	"optimization": f"Add a composite B-Tree index on filtered/joined predicate columns."
	})
	proposed_indexes.append(f"CREATE INDEX CONCURRENTLY idx_{tbl}_query_perf ON {tbl} (created_at DESC, status);")

	# 2. SELECT * on wide tables
	if re.search(r"SELECT\s+\*\s+FROM", query_or_plan, re.IGNORECASE):
	issues.append({
	"severity": "MEDIUM",
	"type": "SELECT_STAR_OVERFETCHING",
	"message": "Wildcard SELECT * forces PostgreSQL to read all toasted columns, bypassing index-only scans.",
	"optimization": "Explicitly project only required columns to enable Index-Only Scans."
	})

	# 3. Missing LIMIT in high-volume query
	if ("select " in query_or_plan.lower()) and ("limit" not in query_or_plan.lower()) and ("count(" not in query_or_plan.lower()):
	issues.append({
	"severity": "HIGH",
	"type": "UNBOUNDED_RESULT_SET",
	"message": "Query lacks an explicit LIMIT clause, risking client out-of-memory under large result sets.",
	"optimization": "Add 'LIMIT 100' or keyset pagination ('WHERE id > :last_id ORDER BY id LIMIT 50')."
	})

	# 4. Hash Join with high buffer read
	if "hash join" in query_or_plan.lower() and "buffers: shared read" in query_or_plan.lower():
	issues.append({
	"severity": "HIGH",
	"type": "DISK_BUFFER_SPILL_JOIN",
	"message": "Hash Join spilled to disk or read from cold shared buffers due to insufficient work_mem.",
	"optimization": "Increase work_mem for the session: 'SET LOCAL work_mem = '64MB';'."
	})

	score = max(0, 100 - (len(issues) * 25))
	return {
	"engine": target_engine,
	"performance_score": score,
	"status": "OPTIMAL" if score == 100 else "DEGRADED" if score >= 60 else "CRITICAL",
	"total_issues": len(issues),
	"issues": issues,
	"proposed_indexes": proposed_indexes
	}


	def guard_ddl_migrations(migration_code: str) -> Dict[str, Any]:
	"""Scans Alembic and SQL schema migration scripts for dangerous AccessExclusiveLock timeouts, table rewrites, and non-concurrent indexes."""
	findings = []

	# 1. Missing lock_timeout
	if ("op.alter_column" in migration_code or "op.add_column" in migration_code or "op.drop_column" in migration_code) and \
	"lock_timeout" not in migration_code:
	findings.append({
	"severity": "CRITICAL",
	"rule": "MISSING_MIGRATION_LOCK_TIMEOUT",
	"message": "ALTER TABLE statement without SET LOCAL lock_timeout can queue behind slow queries and cause a total connection pool stall.",
	"remediation": "Prepend migration with: op.execute(\"SET LOCAL lock_timeout = '2s';\")"
	})

	# 2. Non-concurrent index creation
	if "op.create_index" in migration_code and "postgresql_concurrently=True" not in migration_code:
	findings.append({
	"severity": "HIGH",
	"rule": "NON_CONCURRENT_INDEX_CREATION",
	"message": "Creating an index without 'postgresql_concurrently=True' holds a SHARE lock, blocking all concurrent writes.",
	"remediation": "Enable autocommit block and specify 'postgresql_concurrently=True' in op.create_index."
	})

	# 3. Adding NOT NULL column without server default
	if "nullable=False" in migration_code and "server_default=" not in migration_code and "op.add_column" in migration_code:
	findings.append({
	"severity": "HIGH",
	"rule": "ADD_NOT_NULL_WITHOUT_DEFAULT",
	"message": "Adding a NOT NULL column without server_default fails on existing non-empty tables and locks writes.",
	"remediation": "Add column as nullable first, backfill data in batches, then alter column to set NOT NULL."
	})

	score = max(0, 100 - (len(findings) * 30))
	return {
	"audit_type": "DDL_MIGRATION_SAFETY",
	"safety_score": score,
	"status": "APPROVED" if len(findings) == 0 else "REJECTED_DANGEROUS_LOCKS",
	"total_findings": len(findings),
	"findings": findings
	}


	def audit_connection_pool(config_or_code: str) -> Dict[str, Any]:
	"""Audits SQLAlchemy Async, asyncpg, and pgBouncer configurations for unclosed sessions, pool exhaustion, and greenlet worker leaks."""
	leaks = []

	# 1. Missing AsyncSession context manager
	if "session = AsyncSession(" in config_or_code and "async with" not in config_or_code:
	leaks.append({
	"severity": "CRITICAL",
	"type": "UNCLOSED_ASYNC_SESSION",
	"message": "AsyncSession initialized without 'async with' context manager. Connections remain checked out of the pool indefinitely.",
	"fix": "Wrap database interactions inside: 'async with AsyncSessionLocal() as session:'."
	})

	# 2. Zero pool recycle / missing timeout
	if "create_async_engine" in config_or_code and "pool_recycle" not in config_or_code:
	leaks.append({
	"severity": "MEDIUM",
	"type": "MISSING_POOL_RECYCLE",
	"message": "create_async_engine lacks pool_recycle parameter. Dead or severed stateful connections cause transient 500 errors.",
	"fix": "Set pool_recycle=1800 (30 minutes) and pool_pre_ping=True."
	})

	# 3. Unbounded max_overflow
	if "max_overflow=0" not in config_or_code and "max_overflow" in config_or_code:
	m = re.search(r"max_overflow\s=\s(\d+)", config_or_code)
	if m and int(m.group(1)) > 50:
	leaks.append({
	"severity": "HIGH",
	"type": "EXCESSIVE_MAX_OVERFLOW",
	"message": f"max_overflow ({m.group(1)}) is dangerously high, risking PostgreSQL max_connections exhaustion.",
	"fix": "Bound max_overflow <= 10 and delegate connection multiplexing to pgBouncer in transaction mode."
	})

	score = max(0, 100 - (len(leaks) * 30))
	return {
	"audit_type": "CONNECTION_POOL_INTEGRITY",
	"pool_health_score": score,
	"status": "HEALTHY" if score >= 80 else "AT_RISK",
	"total_issues": len(leaks),
	"issues": leaks
	}


	def cache_thundering_herd_guard(cache_code: str) -> Dict[str, Any]:
	"""Audits Redis / Memcached caching patterns for cache stampede (thundering herd) risks and recommends probabilistic early expiration."""
	vulnerabilities = []

	# 1. Simple get-or-fetch without locking
	if ("redis.get(" in cache_code or "cache.get(" in cache_code) and \
	("lock(" not in cache_code and "setnx" not in cache_code.lower() and "stale" not in cache_code.lower()):
	vulnerabilities.append({
	"severity": "HIGH",
	"risk": "CACHE_STAMPEDE_THUNDERING_HERD",
	"message": "Cache miss triggers direct database query without distributed locking. Under 5,000 req/s, all concurrent requests hit the DB simultaneously on key expiry.",
	"remediation": "Implement distributed lock (redis.set(key, val, nx=True, ex=5)) or probabilistic early recomputation (XFetch algorithm)."
	})

	# 2. Static TTL without jitter
	if re.search(r"ex\s=\s3600\|ttl\s=\s3600\|expire\s\(\skey\s,\s\d+\s*\)", cache_code) and "random" not in cache_code:
	vulnerabilities.append({
	"severity": "MEDIUM",
	"risk": "SYNCHRONIZED_CACHE_EXPIRY",
	"message": "Static cache TTL without randomized jitter causes batch keys to expire at the exact same second.",
	"remediation": "Add jitter to expiration: ttl = base_ttl + random.randint(0, int(base_ttl * 0.1))."
	})

	score = max(0, 100 - (len(vulnerabilities) * 35))
	return {
	"audit_type": "CACHE_STAMPEDE_DEFENSE",
	"cache_resilience_score": score,
	"status": "PROTECTED" if score == 100 else "VULNERABLE_TO_STAMPEDE",
	"findings_count": len(vulnerabilities),
	"findings": vulnerabilities
	}


	# =====================================================================
	# STANDARD MCP JSON-RPC 2.0 PROTOCOL LOOP (STDIO)
	# =====================================================================

	TOOLS = [
	{
	"name": "diagnose_slow_query",
	"description": "Analyzes PostgreSQL EXPLAIN (ANALYZE, BUFFERS) plans and SQL queries, diagnosing sequential scans and proposing exact concurrent B-Tree/BRIN indexes.",
	"inputSchema": {
	"type": "object",
	"properties": {
	"query_or_plan": {"type": "string", "description": "SQL query or EXPLAIN (ANALYZE, BUFFERS) text to analyze."},
	"target_engine": {"type": "string", "description": "Database engine (postgresql, mysql, clickhouse)", "default": "postgresql"}
	},
	"required": ["query_or_plan"]
	}
	},
	{
	"name": "guard_ddl_migrations",
	"description": "Scans Alembic and SQL schema migration scripts for dangerous table lock patterns (missing lock_timeout, non-concurrent indexes, table rewrites).",
	"inputSchema": {
	"type": "object",
	"properties": {
	"migration_code": {"type": "string", "description": "Alembic Python migration script or raw DDL SQL."}
	},
	"required": ["migration_code"]
	}
	},
	{
	"name": "audit_connection_pool",
	"description": "Audits SQLAlchemy Async, asyncpg, and pgBouncer configs for unclosed sessions, connection pool exhaustion, and greenlet worker leaks.",
	"inputSchema": {
	"type": "object",
	"properties": {
	"config_or_code": {"type": "string", "description": "Database connection and session handling code snippet."}
	},
	"required": ["config_or_code"]
	}
	},
	{
	"name": "cache_thundering_herd_guard",
	"description": "Audits Redis / Memcached caching patterns for cache stampede (thundering herd) risks, missing distributed locks, and synchronized TTL expiry.",
	"inputSchema": {
	"type": "object",
	"properties": {
	"cache_code": {"type": "string", "description": "Application caching logic to inspect."}
	},
	"required": ["cache_code"]
	}
	}
	]


	def handle_request(req: Dict[str, Any]) -> Optional[Dict[str, Any]]:
	method = req.get("method")
	req_id = req.get("id")
	params = req.get("params", {})

	if method == "initialize":
	return {
	"jsonrpc": "2.0",
	"id": req_id,
	"result": {
	"protocolVersion": "2024-11-05",
	"capabilities": {"tools": {}},
	"serverInfo": {
	"name": "emgena-dataops-query-performance",
	"version": "2.0.0"
	}
	}
	}
	elif method == "tools/list":
	return {
	"jsonrpc": "2.0",
	"id": req_id,
	"result": {"tools": TOOLS}
	}
	elif method == "tools/call":
	tool_name = params.get("name")
	args = params.get("arguments", {})

	result_data = {}
	if tool_name == "diagnose_slow_query":
	result_data = diagnose_slow_query(args.get("query_or_plan", ""), args.get("target_engine", "postgresql"))
	elif tool_name == "guard_ddl_migrations":
	result_data = guard_ddl_migrations(args.get("migration_code", ""))
	elif tool_name == "audit_connection_pool":
	result_data = audit_connection_pool(args.get("config_or_code", ""))
	elif tool_name == "cache_thundering_herd_guard":
	result_data = cache_thundering_herd_guard(args.get("cache_code", ""))
	else:
	return {
	"jsonrpc": "2.0",
	"id": req_id,
	"error": {"code": -32601, "message": f"Tool not found: {tool_name}"}
	}

	return {
	"jsonrpc": "2.0",
	"id": req_id,
	"result": {
	"content": [{"type": "text", "text": json.dumps(result_data, indent=2, ensure_ascii=False)}]
	}
	}
	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("Emgena DataOps Query Performance MCP Server Health: OK")
	res = diagnose_slow_query("Seq Scan on orders (cost=0.00..1524.00 rows=50000 width=32)")
	print("Test Query Result:", res.get("status"))
	return

	# Standard MCP JSON-RPC line-by-line stdio loop
	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()