Logdetective MCP Server

Log Detective MCP

MCP server implementing core log analysis tools of Log Detective for use by other agents.

The server uses the Drain3 algorithm to cluster log messages into templates and extract a representative subset of snippets, reducing large logs to a manageable size for downstream analysis.

Installation

Requires Python 3.11+.

pip install .

Or with uv:

uv pip install .

Usage

Running the server

logdetective-mcp

Or directly:

python -m logdetective_mcp.main

MCP client configuration

Claude Code

claude mcp add logdetective -- logdetective-mcp

Claude Desktop

Add to your Claude Desktop configuration file:

{
  "mcpServers": {
    "logdetective": {
      "command": "logdetective-mcp"
    }
  }
}

Tools

extract_log_snippets

Extracts representative log snippets using Drain3 clustering. The tool chunks the log into logical messages, clusters similar messages, and returns one representative snippet per cluster.

Log content can be provided in three ways (exactly one must be used):

The input schema for both tools uses oneOf to require exactly one nonempty source field. Omitted source fields and fields set to "" do not count as a source. This matches the rule checked when a tool runs. The advertised tool definitions conform to the MCP 2025-11-25 Tool schema.

Optional parameters:

Returns a list of Snippet objects, each with line_number (position in the original log) and text (the extracted snippet content). MCP structured output wraps this list as {"result": [...]}; the advertised output schema describes that object.

extract_python_tracebacks

Extracts Python tracebacks from the log, including chained exceptions.

For both extractor tools, context follows grep's -C behavior: the requested number of surrounding log lines is included on each side when available. The length limit applies to the extracted snippet or traceback; context lines are additional, so the returned text can be longer than the limit. With context enabled, line_number points to the first returned line. Values outside 0-50 are rejected.

If a traceback is truncated, the extractor keeps its beginning and end with a marker when max_traceback_len permits; smaller limits keep only a prefix. Following context starts after the original traceback, without restoring its omitted middle. If a log line is split into chunks by max_snippet_len, each selected chunk keeps its own text when context is added. Neighboring lines do not replace it with the complete long line. Context boundaries follow the chunk's original source lines, including blank lines omitted from the rendered chunk.

Size limits

All downloaded inputs (via log_url) are limited to 100 MB regardless of format. Downloads are streamed in chunks and aborted as soon as the limit is exceeded.

Archives read via log_path are also checked against the 100 MB limit during decompression.

Compressed file support

Both tools transparently decompress archived log files. Supported formats:

• Single-file compression: .gz, .bz2, .xz, .lzma
• Archives: .zip, .tar, .tar.gz, .tar.bz2, .tar.xz

Format is detected from the file extension (or URL path). Archives must contain exactly one file.

Zip bomb protection is enforced via:

• Maximum decompressed size of 100 MB
• Maximum compression ratio of 100:1
• Pre-check of declared sizes in zip/tar headers
• No recursive decompression (nested archives are not unpacked)

Contributing

All changes to this repository must pass pre-commit checks and tests.

uv run pytest tests/

To inspect the JSON schemas returned by the installed server, run the check script with the project's virtual environment. It starts the server over MCP stdio, validates both tool schemas and structured results, and compares the returned tool definitions with the files in schemas/:

.venv/bin/python scripts/check_mcp_schemas.py

After an intentional schema change, regenerate those files with:

.venv/bin/python scripts/check_mcp_schemas.py --write