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detect_patterns

Scans text columns in database tables to recognize patterns such as emails, phone numbers, URLs, tax IDs, and dates, turning unstructured data into queryable insights.

Instructions

Распознавание паттернов в тексте: email, телефон, URL, ИНН, даты.

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": {
      "table_name": "<table_name>"
    },
    "name": "detect_patterns"
  }
}

Input Schema

Name	Required	Description	Default
`columns`	No
`table_name`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Sep 1, 2026

TDQS

C2.6/5.0

Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states that patterns are recognized in text but does not say whether the operation is read-only, whether it scans the whole table or only specified columns, or what kind of results are returned beyond the existence of an output schema.

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 a single short sentence with no filler and is front-loaded with the core function. While it omits important usage and parameter details, the concise structure itself is well-formed.

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

Completeness2/5

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

For a tool with 2 parameters and many similar sibling tools, the description is incomplete: it lacks usage guidance, parameter semantics, and any clarification of behavior beyond pattern recognition. The presence of an output schema reduces the need to describe return values, but the description still does not provide enough context for confident invocation.

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

Parameters1/5

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

Schema description coverage is 0%, and the description adds nothing about the parameters. The input schema shows only bare property names (table_name, columns), and the tool description does not explain how columns should be specified or what values are expected, leaving an agent to guess the format.

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

Purpose4/5

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

The description uses a clear verb ('распознавание') and identifies the resource being acted on: text patterns. It gives concrete examples (email, phone, URL, INN, dates) that make the tool's scope understandable, which is enough to distinguish its general intent from siblings even if it does not explicitly name alternatives.

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

Usage Guidelines2/5

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

There is no guidance about when to use this tool instead of similar siblings like scan_pii, detect_anomalies, or data_quality_report. No conditions, exclusions, or alternative tool names are mentioned.

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