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Fato07

Log Analyzer MCP

by Fato07
OverviewSchemaRelated ServersScoreDiscussions
Python
Local

log_analyzer_multi

Read-onlyIdempotent

Analyze multiple log files to cross-reference entries, correlate events within a time window, or compare error patterns across files for debugging.

Instructions

Analyze multiple log files together for cross-file debugging.

Supports three operations:
- merge: Interleave entries by timestamp (like 'sort -m')
- correlate: Find events happening across files within time window
- compare: Diff error patterns between files

Args:
    file_paths: List of log file paths to analyze (2-10 files)
    operation: Analysis operation - 'merge', 'correlate', or 'compare' (default: 'merge')
    time_window: Time window in seconds for correlation (1-3600, default: 60)
    max_entries: Maximum entries to return (100-5000, default: 1000)
    response_format: Output format - 'markdown' or 'json'

Returns:
    Combined analysis results based on the selected operation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
file_pathsYes
operationNomerge
time_windowNo
max_entriesNo
response_formatNomarkdown

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Tool Definition Quality

A4.3/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare safety, but description adds operational details (operations, parameter constraints, output format options) that go beyond annotations. No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Concise and well-organized: intro, bullet list of operations, Args table, Returns. No redundant information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Coverage is strong for parameters and operations; output schema exists. Lacks prerequisites like file format or access permissions, but minimal impact given the tool's simplicity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema has no descriptions (0% coverage), but the description provides clear semantics for all parameters including constraints (e.g., 2-10 files, 1-3600 seconds), defaults, and options.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states 'Analyze multiple log files together for cross-file debugging' and details three specific operations, making the purpose explicit and distinct from single-file siblings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Lists operations but does not explain when to use this tool versus sibling tools like 'log_analyzer_correlate' or 'log_analyzer_diff'. Some guidance on context but lacks explicit exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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