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Streams NDJSON log triage: query patterns, detect anomalies, summarize timelines without full load.
Streams NDJSON log triage: query patterns, detect anomalies, summarize timelines without full load.
Valid MCP server (2 strong, 4 medium validity signals). No known CVEs in dependencies. Package registry verified. Imported from the Official MCP Registry. Trust signals: trusted author (17/17 approved).
6 files analyzed ยท 1 issue found
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Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-vola-trebla-ndjson-local-log-triage-mcp": {
"args": [
"-y",
"ndjson-local-log-triage-mcp"
],
"command": "npx"
}
}
}From the project's GitHub README.
Your service just crashed. The log file is 2GB. Your AI agent can't help.
MCP server that stream-parses NDJSON log files without loading them into memory โ filter by pattern, detect error spikes via Z-score analysis, summarize severity timelines by time window.
A service crashes at 3am. The log file is app.log.ndjson and it's 2GB. You ask your agent to find what caused the spike in errors around 03:17. The agent can't read 2GB. It can't even try.
ndjson-local-log-triage-mcp streams the file line by line โ never loading it into memory โ and gives the agent exactly the slice it needs.
query_log_patternFilter log entries by a field/value match. Returns up to N matching entries, streaming the file without loading it entirely. Pass lineStartPattern (e.g. "^{") to reconstruct multiline stack traces silently dropped by the default parser.
Log Query Results
File: /var/log/app.log.ndjson
Filter: service contains "auth"
Lines read: 847,293
Matches: 50 (limit 50 reached)
{"timestamp":"2025-01-15T03:17:02Z","level":"error","service":"auth","msg":"token validation failed","userId":"u_abc123"}
...
detect_error_anomaliesZ-score frequency analysis. Buckets errors by time window, computes mean + stddev, flags windows where the error rate is anomalously high.
Error Anomaly Detection
File: /var/log/app.log.ndjson
Window: 5min
Z-score cutoff: 2.0
Baseline: mean=3.2 errors/window, stdDev=1.8
Anomalies found: 2
[z=4.71] 2025-01-15T03:15:00.000Z 23 errors
[z=2.33] 2025-01-15T03:20:00.000Z 9 errors
summarize_log_timelineChronological aggregation of errors, warnings, and info counts per time window. Quick visual of where the incident is.
Pass adaptive: true to auto-scale bucket size to actual event density and zoom in on the peak error window at 10ร finer resolution.
Log Timeline Summary
File: /var/log/app.log.ndjson
Window: 5min
Buckets: 48
Time (UTC) Errors Warnings Info Other
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
2025-01-15 03:00:00Z 2 8 142 0
2025-01-15 03:05:00Z 1 5 138 0
2025-01-15 03:10:00Z 3 9 141 0
! 2025-01-15 03:15:00Z 23 14 119 0
2025-01-15 03:20:00Z 9 11 133 0
correlate_requestReconstructs a distributed trace from multiple NDJSON log files. Given a trace_id, collects all correlated events in chronological order across all files and surfaces the services involved and total duration.
Request Correlation
Trace ID: trace-8f7a9b2c
Files scanned: 2
Events found: 10
Services involved: api, worker
Duration: 890ms
[2025-01-15T14:00:00.001Z] api {"level":"info","msg":"incoming request",...}
[2025-01-15T14:00:00.045Z] api {"level":"info","msg":"auth token validated",...}
[2025-01-15T14:00:00.112Z] worker {"level":"info","msg":"job queued",...}
...
{
"mcpServers": {
"log-triage": {
"command": "npx",
"args": ["-y", "ndjson-local-log-triage-mcp"]
}
}
}
"Analyze /var/log/app.log.ndjson โ summarize the error timeline in 5-minute windows, detect any anomalous spikes, and show me the error entries around the spike."
Works great alongside:
MIT
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