Log Analyzer MCP

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log_analyzer_trace

Read-onlyIdempotent

Analyze log files by extracting and grouping trace/correlation IDs to visualize request flows across your system, supporting OpenTelemetry, X-Request-ID, AWS X-Ray, and UUID formats.

Instructions

Extract and follow trace/correlation IDs across log entries.

Automatically detects trace IDs (OpenTelemetry, X-Request-ID, AWS X-Ray, UUID)
and groups related log entries to show request flows through your system.

Args:
    file_path: Path to the log file to analyze
    trace_id: Specific trace ID to filter for (None for all traces)
    max_traces: Maximum number of trace groups to return (1-500, default: 100)
    max_lines: Maximum lines to process (100-100000, default: 10000)
    response_format: Output format - 'markdown' or 'json'

Returns:
    Trace groups showing request flows, including trace ID types detected,
    entry counts, time spans, and error indicators.

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_trace"
  }
}

Input Schema

Name	Required	Default
`file_path`	Yes
`trace_id`	No
`max_traces`	No
`max_lines`	No
`response_format`	No	markdown

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedJan 30, 2026

TDQS

A4.4/5.0

Behavior4/5

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

Annotations already declare the tool as readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds behavioral context beyond these annotations: it explains automatic detection of trace ID formats (OpenTelemetry, X-Request-ID, AWS X-Ray, UUID) and grouping of related log entries. This enriches transparency without contradicting annotations.

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 well-structured with a clear opening statement, followed by a bullet list of arguments and a return summary. It is concise (each sentence adds value) and front-loaded with the core purpose, making it efficient for an AI agent to parse.

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

Completeness5/5

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

Given the tool's complexity (5 parameters, output schema exists), the description covers input, output, and behavior comprehensively. It explains how trace IDs are detected and grouped, and describes the return value structure (trace groups, IDs, counts, time spans, errors). The output schema likely handles detailed return formatting, so this high-level description is sufficient.

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 description coverage is 0%, but the description provides detailed semantics for all 5 parameters: file_path (path to file), trace_id (specific ID to filter), max_traces (range 1-500, default 100), max_lines (range 100-100000, default 10000), response_format (options 'markdown' or 'json'). This adds crucial meaning beyond the raw schema, making the tool easy to use correctly.

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: 'Extract and follow trace/correlation IDs across log entries' and explains it detects trace IDs and groups related entries to show request flows. This distinguishes it from siblings like log_analyzer_search or log_analyzer_summarize, which focus on different aspects of log analysis.

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 usage for tracing request flows but does not explicitly state when to use this tool versus alternatives among the 13 sibling tools. No when-not-to-use or comparative guidance is provided, leaving the agent to infer based on the tool's focus on trace IDs.

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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