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tibero-mcp

tibero-mcp

by jdk1021

search_columns

Read-only

Finds database columns by matching column names or comments across a schema, with paged results for browsing large catalogs.

Instructions

Find columns across a schema by name or comment, e.g. '주문' or 'ORDER_DT'. Results are paged.

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": {
      "keyword": "<keyword>"
    },
    "name": "search_columns"
  }
}

Input Schema

NameRequiredDescriptionDefault
offsetNoRows to skip; pass next_offset from a truncated result to page.
schemaNoSchema (owner). Defaults to the connected user.
keywordYesText matched against column names and column comments.
max_rowsNoRows per page.
table_searchNoOptional text to narrow the tables searched (name or table comment).

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior3/5

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

Annotations already establish that this is a read-only, closed-world operation. The description adds useful behavioral context that results are paged, but does not discuss matching behavior, limits, or result shape; an output schema covers return values.

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?

Two short, front-loaded sentences with a concrete example and the paging note. Every sentence contributes to understanding how to invoke the tool.

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 existing output schema and annotations, the description covers the core search behavior and paging enough to call the tool correctly. It omits details such as table_search narrowing and schema defaults, but those are documented in the input schema.

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

Parameters3/5

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

Schema description coverage is 100%, so the schema already explains keyword matching, schema scoping, table_search, offset, and max_rows. The description reinforces keyword matching and paging but adds no semantics beyond the schema, such as case sensitivity or matching rules.

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 states a specific verb and resource: find columns across a schema by name or comment, and gives concrete keyword examples. It does not explicitly distinguish itself from siblings such as describe_table or list_tables, so it stops short of a 5.

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?

It implies the use case is keyword search over column names/comments and demonstrates input examples, but gives no when-not guidance or named alternatives. An agent must infer whether to use this instead of describe_table or query.

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