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Log Analyzer MCP

Log Analyzer MCP

by Fato07
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

log_analyzer_suggest_format

Read-onlyIdempotent

Analyze a log file to detect its format with a confidence score, suggest alternative formats if confidence is low, identify unparseable lines with recommendations, and provide custom pattern suggestions for generic parsing.

Instructions

Analyze a log file and suggest the best parsing approach.

Returns detailed format detection information including:
- Detected format with confidence score
- Alternative formats to try if confidence is low
- Sample of unparseable lines with suggestions
- Custom pattern suggestions for generic parser

Args:
    file_path: Path to the log file to analyze
    sample_size: Number of lines to sample for analysis (default: 100)
    response_format: Output format - 'markdown' or 'json'

Returns:
    Format suggestions and analysis results

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "file_path": "<file_path>"
    },
    "name": "log_analyzer_suggest_format"
  }
}

Input Schema

NameRequiredDescriptionDefault
file_pathYes
sample_sizeNo
response_formatNomarkdown

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations indicate the tool is read-only, non-destructive, and idempotent. The description adds behavioral context beyond annotations by detailing what the tool returns (confidence scores, alternative formats, unparseable lines, custom patterns). 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?

The description is concise and front-loaded: the first sentence states the purpose, followed by a bullet list of return items, then parameter explanations. Every sentence adds value with no redundancy.

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?

Given the tool's complexity (3 parameters with defaults, annotations present, output schema exists), the description adequately covers what the tool does and returns, listing key output components. It does not explain the output schema but it is not required. Minor improvement could include typical use cases.

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

Parameters4/5

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

The input schema has 0% description coverage, so the description compensates by explaining each parameter's meaning and defaults (e.g., sample_size controls lines sampled, response_format accepts 'markdown' or 'json'). This adds significant value over the bare schema.

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?

The description clearly states the tool's purpose: analyze a log file and suggest the best parsing approach. It lists specific return items (detected format, confidence score, alternatives) that distinguish it from sibling tools like log_analyzer_parse or log_analyzer_suggest_patterns.

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?

The description implies the tool is for determining log format before parsing, but it does not explicitly state when to use it versus alternatives or provide exclusions. No direct comparison with sibling tools is given.

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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