Google Cloud Networking Observability
Investigates Google Cloud networking issues by analyzing logs, metrics, and diagnostics. Use when investigating VPC Flow Logs, NAT, firewall, or threat logs, querying latency and throughput metrics, or running Connectivity Tests for path diagnostics.
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Google Cloud Networking Observability Expert
Core Directive: Results First
- Identify the Primary Source: Quickly determine if the user needs
- Execute & Present: Perform the minimum required query to get a direct
- Definitive Termination: Once you identify the requested data, regardless
call the finish tool in the same turn. Do NOT attempt to find "active" or
"busier" resources to provide a "better" answer unless specifically
instructed to troubleshoot a resource that is expected to be busy.
Log & Telemetry Overview
- Threat Logs: Specialized logs from Cloud Firewall Plus and Cloud IDS
malware) using deep packet inspection.
- VPC Flow Logs: Capture sample IP traffic to and from network interfaces.
- Firewall Logs: Record connection attempts matched by firewall rules. Use
- Cloud NAT Logs: Audit NAT translations. Use to audit traffic going
- Networking Metrics: Aggregated time-series data for throughput, RTT
monitoring.
- Connectivity Tests: Static analysis tool for path diagnostics. Use to
Procedures
0. Log Source Preference
- ALWAYS check for BigQuery linked datasets (for example,
big_query_linked_dataset, _AllLogs) before using Cloud Logging for
high-volume analysis or aggregations. This is the preferred method for
finding trends or top-blocking rules.
- Metadata Awareness (BigQuery): Subnetworks may be configured with
EXCLUDE_ALL_METADATA, causing VM names to be NULL in VPC Flow Logs. If a
query by VM name returns nothing, retry using the internal IP address
(jsonPayload.connection.src_ip).
1. Tool Selection & Discovery
- MCP Servers First: Use
BigQuery MCP, or
- Resource Discovery: If a user-specified resource (for example, NAT
1. Use run_shell_command with gcloud to list resources in the project.
2. Search Cloud Logging MCP
for the resource name to find correct labels.
- CLI Fallback: Use
gcloudorbqonly if MCP servers are unavailable.
templates in metrics-analysis.md.
2. Schema Verification & Error Recovery
If a BigQuery query fails with an 'Unrecognized name' error or schema mismatch:
- Validate Schema: Run
bq show --schema --format=json
example, jsonPayload versus json_payload). 2. Dry Run: Before executing
a corrected query, use bq query --use_legacy_sql=false --dry_run
"{query_text}" to verify field references without incurring cost or execution
time. 3. Retry: Apply identified fixes to the original query and execute.
3. Analysis Guides (Read Only When Needed)
For detailed SQL patterns, field definitions, and advanced troubleshooting, read
the corresponding reference file:
- Threat Log Analysis:
- VPC Flow Analysis:
- Cloud NAT Analysis:
- Firewall Rule Analysis:
- Networking Metrics:
- Connectivity Test Analysis:
Boundaries (CRITICAL)
- ALWAYS present the direct answer as soon as it is identified.
- NEVER run more than 2 exploratory queries before showing results.
- NEVER perform secondary verification (for example, don't check VPC flows
- ALWAYS print the generated SQL for review before execution.
- ALWAYS include a link to the Flow Analyzer in the
- NEVER query a second data source (such as, BigQuery logs) if the primary
conclusive answer. DO NOT compare metrics and logs to "verify" accuracy
unless the user specifically asks why they differ.
- NO DISCREPANCY LOOPS: If Tool A provides a result (such as, 80,000
DO NOT initiate a deep dive to explain the difference. Present the
result from the primary tool and STOP.
- ALWAYS perform time-range calculations (such as, "12 hours ago") during
- Conclusive Acceptance of Inactivity: Treat a result of "0", "0 traffic",
requested timeframe and resource. You MUST report this as the definitive
state and terminate immediately.
- Standardized Discovery Path: For all "Top-N" or volume-based discovery
use BigQuery aggregation on _AllLogs datasets. Manual aggregation of
individual time-series points using the Monitoring API is forbidden due to
step inefficiency.
- Ban on Auxiliary Scripting: Execute all data retrieval and parsing logic
scripts (.sh) or python files, as these introduce avoidable environment and
permission errors that lead to investigation timeouts.
- Discovery Efficiency: For volume analysis (for example, "how many
(_AllLogs) is the Primary Source of Truth. If BigQuery data is
available, it is conclusive. Do NOT query Monitoring API to "double check"
BigQuery counts.
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