io.github.rog0x/log — Model Context Protocol (MCP) Server

This MCP server provides log analysis and processing tools for AI agents. It is exposed via the Model Context Protocol and focuses on parsing common log formats and extracting structured fields for downstream analysis.

🛠️ Key Features

Tools for parse_logs and filter_logs
Parses JSON logs, Apache/Nginx access logs, syslog, and structured logs
Supports custom regex patterns with named capture groups
Extracts timestamp, level, message, source, and additional structured fields
Filters entries by log level (ERROR, WARN, INFO, DEBUG), date range (ISO 8601), keyword search (case-insensitive), and regex matching

🚀 Use Cases

Parsing application and infrastructure logs into structured records
Filtering logs for specific time windows or severity levels
Searching logs for keywords and matching patterns for investigation

⚡ Developer Benefits

Provides consistent parsed fields (timestamp, level, message, source) for agent workflows
Supports structured log extraction and regex-based customization

⚠️ Limitations

The available readme excerpt only describes partial filter_logs behavior (“Sou” cut off), so additional filters are not specified here.

Tools

parse_logs

Parse log content in common formats: JSON logs, Apache/Nginx access logs, syslog, and structured logs. Supports custom regex patterns with named capture groups. Extracts timestamp, level, message, source, and additional structured fields.

filter_logs

Filter parsed log entries by:

Log level (ERROR, WARN, INFO, DEBUG)

Date range (ISO 8601)

Keyword search (case-insensitive)

Regex pattern matching

Source/logger name

Returns matched entries along with level occurrence counts.

analyze_logs

Analyze log patterns to surface operational insights:

Top 10 most frequent errors (normalized)

Error rate over time (hourly buckets)

Request volume per hour

Slowest endpoints (from HTTP access logs)

Anomaly detection (sudden spikes or drops using standard deviation)

format_logs

Convert between log formats and produce reports:

JSON to plain text, plain to JSON, CSV, or table

ANSI colorization by log level

Structured field extraction

Summary report generation

log_stats

Compute comprehensive log statistics:

Total and non-empty line counts

Level distribution with percentages

Date range coverage

Unique IP addresses and request counts

Top user agents

HTTP status code distribution

Throughput (entries per second)

Supported Log Formats

Format

Example

JSON

{"timestamp":"2024-01-15T10:30:00Z","level":"ERROR","message":"Connection failed"}

Apache/Nginx

127.0.0.1 - frank [10/Oct/2000:13:55:36 -0700] "GET /index.html HTTP/1.1" 200 2326

Syslog

Jan 1 00:00:00 hostname process[1234]: Something happened

Structured

2024-01-15T10:30:00.000Z [ERROR] [ModuleName] Something failed

Custom

Any format using a regex with named capture groups

io.github.rog0x/log