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Log Detective MCP

Log Detective MCP

Official
by fedora-copr
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault

No arguments

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tools
{
  "listChanged": false
}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
extract_log_snippetsA

Extract representative log snippets using Drain3 clustering.

Analyzes log text by chunking it into logical messages, clustering
similar messages using the Drain algorithm, and returning one
representative snippet per cluster.

Exactly one of log_text, log_path, or log_url must be provided.

Downloaded inputs are limited to 100 MB.
Compressed files (.gz, .bz2, .xz, .zip, .tar, .tar.gz, .tar.bz2,
.tar.xz) are decompressed transparently. Archives must contain
exactly one file. Compression ratios above 100:1 are rejected.

Args:
    log_text: Raw log text to analyze.
    log_path: Path to a log file on disk.
    log_url: HTTP(S) URL to fetch log content from.
    max_clusters: Maximum number of clusters/snippets to extract.
    max_snippet_len: Maximum character length per snippet chunk.
    skip_patterns: Optional list of regex patterns.
        Chunks matching any pattern are excluded before clustering.
    context: Number of lines to include before and after each snippet (0-50).
        These lines are additional to max_snippet_len. Split chunks retain
        their extracted text instead of expanding to the whole source line.
extract_python_tracebacksA

Extract Python tracebacks using specialized heuristic.

Analyzes log text and extracts unbroken Python tracebacks.
Traces are truncated to `max_traceback_len` characters.

Exactly one of log_text, log_path, or log_url must be provided.

Downloaded inputs are limited to 100 MB.
Compressed files (.gz, .bz2, .xz, .zip, .tar, .tar.gz, .tar.bz2,
.tar.xz) are decompressed transparently. Archives must contain
exactly one file. Compression ratios above 100:1 are rejected.

Args:
    log_text: Raw log text to analyze.
    log_path: Path to a log file on disk.
    log_url: HTTP(S) URL to fetch log content from.
    max_traceback_len: Maximum character length per extracted traceback.
    skip_patterns: Optional list of regex patterns.
        Chunks matching any pattern are excluded.
    context: Number of lines to include before and after each traceback (0-50).
        These lines are additional to max_traceback_len. A truncated
        traceback keeps its shortened text; following context begins after
        the original traceback ends.

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A3.9/5.0

Scored across 2 tools

Disambiguation4/5

The two tools target different extraction strategies (Drain3 clustering vs Python traceback heuristics), so their core purposes are clearly distinct. However, they share identical input modes (log_text/log_path/log_url, skip_patterns, context), and a log containing tracebacks could plausibly be fed to either, leaving a small boundary ambiguity an agent must reason about.

Naming Consistency5/5

Both names follow the same `extract_<thing>` snake_case convention with the same verb prefix and noun-phrase object. Parameter naming is also mirrored across the two tools (max_*_len, skip_patterns, context), reinforcing predictability.

Tool Count3/5

Two tools is thin for a server branded as 'Log Detective' — the surface is essentially one extraction family with two strategies. It is not incoherent, but there is little room for an agent to do anything beyond pulling snippets or tracebacks.

Completeness3/5

Input handling is thorough (text/path/URL, transparent decompression, safety limits) and both tools mirror it consistently. But the lifecycle stops at extraction: there is no summarization, severity/error-level grouping, keyword or pattern-include extraction, or structured output option, which leaves gaps for a diagnostic-oriented server.

Maintenance

ActivityMaintained
ResponsivenessNo recent community issues

Resources