About log
@rog0x/mcp-log-tools is an MCP server for AI agents to process and analyze logs in common formats or custom regex-based formats. It provides filtering, statistics, anomaly detection, and format conversion.
Key features
- 01Parse common and custom log formats
- 02Filter by level, date, and keyword
- 03Summarize errors and request trends
- 04Detect unusual log-volume changes
- 05Convert formats and generate reports
Use cases
- 01Filter error logs for a date range
- 02Analyze HTTP traffic and slow endpoints
- 03Convert logs to CSV or tables
MCP support
It exposes log parsing, filtering, analysis, formatting, and statistics tools to AI agents through MCP.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
Tools
5Saved tool list · updated 2w ago
| Parameter | Type | Required | Description |
|---|---|---|---|
| limit | number | No | Maximum number of entries to return |
| format | enum | No | Log format to use for parsing. Use 'auto' to detect automatically.auto | json | apache | nginx | syslog | custom |
| content | string | Yes | Raw log content to parse |
| custom_pattern | string | No | Custom regex pattern with named groups: (?<timestamp>), (?<level>), (?<message>), (?<source>) |
Details
From the project README@rog0x/mcp-log-tools
Log analysis and processing tools for AI agents, exposed via the Model Context Protocol (MCP).
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)
Setup
npm install
npm run build
Usage with Claude Desktop
Add to your claude_desktop_config.json:
{
"mcpServers": {
"log-tools": {
"command": "node",
"args": ["path/to/mcp-log-tools/dist/index.js"]
}
}
}
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 |
License
MIT
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.