explore-datasources

name	explore-datasources
description	Discover what datasources, metrics, labels, and log streams are available in a Grafana instance. Use when the user asks what data exists, what metrics are available, what services are being monitored, or needs to find a datasource UID.

Datasource Discovery

If gcx is not configured, see the setup-gcx skill first.

Instructions

Step 1: List Available Datasources

Start by identifying all datasources in the Grafana instance.

# List all datasources
gcx datasources list

# Filter by type if you know what you need
gcx datasources list --type prometheus
gcx datasources list --type loki

Expected output: Table showing UID, NAME, TYPE, URL, and DEFAULT columns.

Important: Always use the UID (not the name) in subsequent commands.

Step 2: Explore Datasource Contents

Choose the appropriate exploration path based on datasource type.

For Prometheus Datasources

# List all available labels
gcx metrics labels -d <datasource-uid>

# Get values for a specific label to understand what's being monitored
gcx metrics labels -d <datasource-uid> --label job

# List all available metrics with descriptions
gcx metrics metadata -d <datasource-uid>

# Check what systems are being scraped
gcx metrics query -d <datasource-uid> 'up'

Expected output: Tables showing labels, metrics, or query results depending on command.

For Loki Datasources

# List all available labels
gcx logs labels -d <datasource-uid>

# Get values for a specific label
gcx logs labels -d <datasource-uid> --label job

# List log streams matching a selector (required)
gcx logs series -d <datasource-uid> -M '{job="varlogs"}'

Expected output: Tables showing labels or log stream series.

Note: The series command requires at least one -M (match) selector using LogQL syntax.

Note: gcx logs labels lists indexed stream labels — the keys valid inside a {...} selector. Loki also has structured metadata (per-line, e.g. detected_level) and parsed labels that are NOT indexed and must be filtered after a pipe ({job="x"} | detected_level="error"), never inside {}. In gcx logs query output they appear under each entry's structuredMetadata / parsed (not the stream map).

Step 3: Test Queries (Optional)

Once you've identified available data, verify with a test query.

# For Prometheus - instant query (current value)
gcx metrics query -d <datasource-uid> 'up'

# For Prometheus - instant query at a specific point in time
gcx metrics query -d <datasource-uid> 'rate(http_requests_total[5m])' --time 2026-05-14T12:00:00Z

# For Prometheus - range query (time series over a window)
gcx metrics query -d <datasource-uid> 'rate(http_requests_total[5m])' --from now-1h --to now

Choosing instant vs range: Use --time for a value at a moment ("what is the current rate?", "what was the rate an hour ago?"). Use --from/--to for anything across a window — peak detection, totals, trends, or ranking over time. For point-in-time comparisons, run two --time queries rather than a range. If the PromQL uses increase(), max_over_time(), or similar over a long window (hours/days), always use --from/--to — the range lets you verify the full series and aggregate with sum by (label), not count ... > 0 which loses the magnitude.

Expected output: Table showing metric values with labels and timestamps.

Step 4: Set Default Datasource (Optional)

To avoid passing -d <uid> repeatedly, configure defaults:

# Set default Prometheus datasource
gcx config set contexts.<context-name>.default-prometheus-datasource <uid>

# Set default Loki datasource
gcx config set contexts.<context-name>.default-loki-datasource <uid>

After setting defaults, you can omit the -d flag in datasource commands.

Examples

Example 1: Finding HTTP metrics

User says: "What HTTP metrics are available?"

Actions:

1. List Prometheus datasources: gcx datasources list --type prometheus
2. Get datasource UID from output
3. Search for HTTP metrics: gcx metrics metadata -d <uid> -o json 2>/dev/null | python3 -c "import json,sys; d=json.load(sys.stdin)['data']; [print(k,d[k][0]['type'],d[k][0]['help']) for k in sorted(d) if 'http' in k]"
4. Get details on specific metric: gcx metrics metadata -d <uid> --metric http_requests_total

Result: Metric name, type (counter/gauge), and help text showing what the metric measures.

Example 2: Discovering which services are logging to Loki

User says: "What applications are sending logs to Loki?"

Actions:

1. List Loki datasources: gcx datasources list --type loki
2. Get datasource UID from output
3. List label names: gcx logs labels -d <uid>
4. Get job values: gcx logs labels -d <uid> --label job
5. List streams for a specific job: gcx logs series -d <uid> -M '{job="varlogs"}'

Result: List of job names and their associated log streams.

Example 3: Troubleshooting missing dashboard data

User says: "My dashboard shows no data for service X"

Actions:

1. Verify datasource exists: gcx datasources get <uid>
2. Verify labels exist: gcx metrics labels -d <uid> --label job
3. Check if service is being scraped: gcx metrics query -d <uid> 'up{job="service-x"}'

Result: Identifies whether datasource is misconfigured, service isn't being scraped, or label selectors are wrong.

Example 4: Finding logs for a specific namespace

User says: "Show me all log streams from the production namespace"

Actions:

1. Get Loki datasource UID: gcx datasources list --type loki
2. Verify namespace label exists: gcx logs labels -d <uid> --label namespace
3. List all streams in namespace: gcx logs series -d <uid> -M '{namespace="production"}'

Result: Table showing all label combinations for log streams in the production namespace.

Troubleshooting

Error: "datasource UID is required"

Cause: The -d flag was omitted and no default datasource is configured.

Solution:

# Option 1: Pass the UID explicitly
gcx metrics labels -d <datasource-uid>

# Option 2: Set a default datasource
gcx config set contexts.<context-name>.default-prometheus-datasource <uid>

Error: "at least one --match selector is required"

Cause: The loki series command was called without a -M flag.

Solution: Loki series requires at least one LogQL selector:

# Correct
gcx logs series -d <uid> -M '{job="varlogs"}'

# Wrong - will fail
gcx logs series -d <uid>

Error: "parse error on line 1, column X: bare " in non-quoted-field"

Cause: Shell is interpreting quotes in the LogQL selector incorrectly.

Solution: Use single quotes around the entire selector:

# Correct - single quotes outside
gcx logs series -d <uid> -M '{name="value", cluster="prod"}'

# Wrong - shell interprets quotes incorrectly
gcx logs series -d <uid> -M {name="value"}

Error: "datasource.prometheus.datasource.grafana.app "" not found"

Cause: The datasource UID doesn't exist or you don't have access to it.

Solution:

1. List datasources to verify UID: gcx datasources list
2. Check you're using the correct context: gcx config current-context
3. Verify datasource exists: gcx datasources get <uid>

No output from labels/series commands

Cause: Datasource has no data or hasn't scraped/ingested anything yet.

Solution:

1. For Prometheus: Check targets are active: gcx metrics query -d <uid> 'up'
2. For Loki: Verify labels exist: gcx logs labels -d <uid>
3. Check datasource URL is reachable: gcx datasources get <uid>

Advanced Usage

For detailed patterns and advanced discovery workflows, see:

• references/discovery-patterns.md - Common discovery patterns and workflows (jq filtering, cross-datasource search, graph output)
• references/logql-syntax.md - gcx-specific selector rules for logs series (OR via multiple -M flags, shell quoting, indexed vs structured-metadata labels)

Output Formats

All commands support -o json or -o yaml for programmatic use:

# Get JSON output for piping to jq
gcx metrics labels -d <uid> -o json

# Example: Count total metrics
gcx metrics metadata -d <uid> -o json 2>/dev/null | python3 -c "import json,sys; print(len(json.load(sys.stdin)['data']))"

Default output is table format for human readability.