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@jarroba/mcp-logs
Advanced tools
MCP server (stdio, local) that lets an AI read a log file it could never fit in its context: a 200 MB CI log. Detects the format, counts EVENTS (a 40-line stack trace is one), resolves root causes, groups lines into templates, answers when it started, and
An MCP server that lets your AI read a log it could never fit in its context.
claude mcp add jarroba-logs npx -y @jarroba/mcp-logs
// Claude Desktop, Cursor, Cline, VS Code…
{ "mcpServers": { "jarroba-logs": { "command": "npx", "args": ["-y", "@jarroba/mcp-logs"] } } }
11 tools · ~3.3k tokens of context · stdio, runs on your machine · nothing is uploaded.
Every log tool for an AI takes pasted text. A production or CI log is tens or hundreds of MB, so pasting is not an option — and the interesting part is never the part you would have pasted.
These tools take a file path. The server runs in Node, so it opens the file itself and hands back only what fits. An agent debugging a red CI run cannot read the log; it can ask questions about it.
log_analyze | Format, number of events (a 40-line stack trace is one), levels, time range, errors with their root cause resolved, most frequent patterns. |
log_trends | When did it start. Compares each pattern against itself, not against total volume — in a busy service everything rises at 9am and nothing happened. Returns what spiked, what is new, and what stopped appearing: the last one raises no counter anywhere and is often the worse news. |
log_around | What else clustered around a moment. What started before is a candidate explanation; what came after is usually a consequence. Correlation is not causation, and order is what separates them. |
log_patterns | Groups lines into templates (Drain): "connected to 10.0.0.1" and "connected to 10.0.0.7" are one message. Also returns the rare ones, which is usually what you were looking for. |
log_reduce | Cuts to a character budget keeping errors with their full trace, the window before, and whatever appears only once. Repeats collapse to [xN]. For handing a big log to a model without spending the context on the same line repeated. |
log_stacktrace | Java/JVM, Python, Node, Go, .NET, Rust, Ruby, PHP: the Caused by chain, the root cause, and the first frame of your code — inferred from the trace if you do not say. |
log_lint | Whether the log is well written, not what happened: secrets printed, one line flooding the file, timestamps with no time zone (which make two machines impossible to correlate), the clock going backwards, no request id. Secrets come back masked. |
log_convert | JSON / logfmt / syslog 5424 / CSV / OpenTelemetry / ECS — saying up front what the conversion loses. |
log_rotation | Puts rotated logs in time order. Rotation numbering runs backwards against time (app.log.3 is older than app.log.1) and alphabetically app.log.10 sorts before app.log.9. Use it first, or you read them backwards. |
log_profile_checklog_profile_suggest | A reusable profile describing how your team's logs look. check returns every error with its exact field, so a model can correct itself in one pass. |
log_profile_suggest says so instead of filling it in.Transport is stdio: your MCP host launches this as a subprocess and talks over stdin/stdout.
There is no endpoint, no account and no telemetry. npx downloads the package from npm to your
machine the first time; after that nothing goes out.
Two things worth knowing rather than discovering:
Every tool declares MCP annotations: all are readOnlyHint: true, and all are openWorldHint: true because the answer comes from the disk rather than from the arguments.
Part of Jarroba Tools — the same engine as the web tools, so the answer
is identical. Want everything (90 tools, databases, diagrams, test auditing, SPARQL)?
@jarroba/mcp.
MIT — see LICENSE. Source: github.com/Invarato/JarrobaTools.
FAQs
MCP server (stdio, local) that lets an AI read a log file it could never fit in its context: a 200 MB CI log. Detects the format, counts EVENTS (a 40-line stack trace is one), resolves root causes, groups lines into templates, answers when it started, and
We found that @jarroba/mcp-logs demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.

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