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Log Analyzer MCP

Log Analyzer MCP

by Fato07
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

log_analyzer_scan_sensitive

Read-onlyIdempotent

Scan log files for sensitive data like PII, credentials, API keys, and tokens. Redact matches or filter by category to control results.

Instructions

Detect sensitive data in logs (PII, credentials, API keys).

Scans log files for potentially sensitive information including:
- Email addresses
- Credit card numbers (Visa, MasterCard, Amex)
- API keys and tokens (AWS, GitHub, Slack, generic)
- Passwords in URLs or config
- Social Security Numbers (SSN)
- JWT and Bearer tokens
- Database connection strings
- Private key markers
- Phone numbers
- IP addresses (optional)

Args:
    file_path: Path to the log file to scan
    redact: Redact sensitive data in output (default: False)
    categories: Filter to specific categories. Options:
               email, credit_card, api_key, token, password,
               ssn, ip_address, phone, connection_string, private_key
    include_ips: Include IP address detection (default: False)
    max_matches: Maximum matches to return (1-500, default: 100)
    max_lines: Maximum lines to scan (1-1000000, default: 100000)
    response_format: Output format - 'markdown' or 'json'

Returns:
    Sensitive data scan results with matches and statistics.

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_scan_sensitive"
  }
}

Input Schema

NameRequiredDescriptionDefault
file_pathYes
redactNo
categoriesNo
include_ipsNo
max_matchesNo
max_linesNo
response_formatNomarkdown

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations declare readOnlyHint true and destructiveHint false, indicating no side effects. The description reinforces this by stating it 'scans log files' and 'returns results and statistics.' It adds specifics like optional redaction and IP detection, providing behavioral context beyond the annotations.

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

Conciseness4/5

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

The description is front-loaded with the core purpose, followed by a structured list of arguments and return description. Every sentence adds value, though it is slightly lengthy. It earns a 4 because it balances completeness with readability.

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 has 7 parameters, a required file_path, and no nested objects, the description covers all parameters, their options, and the return value. An output schema exists but is not provided; the description still states that returns contain matches and statistics, which together with the schema provides complete information.

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?

With 0% schema description coverage, the description compensates fully by explaining each parameter in detail: file_path, redact, categories (with options list), include_ips, max_matches (range 1-500), max_lines (range 1-1000000), response_format. This adds significant meaning beyond the schema's type and default values.

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 opens with a clear verb-object pair 'Detect sensitive data in logs' and enumerates specific categories (PII, credentials, API keys). It distinguishes this tool from siblings like log_analyzer_search or log_analyzer_extract_errors by focusing exclusively on sensitive data detection.

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

Usage Guidelines4/5

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

The description details when to use the tool via its parameter descriptions (e.g., categories filter, max_matches), but does not explicitly contrast it with sibling tools or state when not to use it. However, the context signals that no other sibling covers sensitive data scanning, so the use case is well implied.

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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