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lu-query

lu-query

by zademy
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TypeScript
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find_duplicates

Identify duplicate value groups across one or more table columns, ignoring NULLs, and return counts with a sample ROWID for groups meeting a minimum occurrence threshold.

Instructions

Find duplicate value groups in one or more columns of a table (excluding NULLs). Groups at or above minCount (default 2) come back with a COUNT and a sample ROWID.

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": "<table>",
      "columns": "<columns>"
    },
    "name": "find_duplicates"
  }
}

Input Schema

NameRequiredDescriptionDefault
tableYesTable name.
schemaNoSchema (owner) of the table. Optional — defaults to the server's current schema; omit rather than guess.
columnsYesColumns to compare together.
minCountNoMinimum occurrences of a group. Defaults to 2.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.16.3

TDQS

A3.8/5.0
Behavior3/5

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

With no annotations, the description carries the full burden, and it does disclose useful traits: NULLs are excluded, the minCount threshold is applied, and the return includes a COUNT plus a sample ROWID. However, it says nothing about read-only vs mutation semantics, permissions, or cost on large tables, so the behavioral picture is only partially covered.

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 tightly written sentences with zero filler, and the core operation is front-loaded before the threshold/return detail. Every clause carries information not already implied by the name.

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?

There is no output schema and no annotations, but the description partially compensates by stating the return contents (COUNT and sample ROWID) and the NULL/minCount semantics. Only the read-only guarantee and any performance caveat are left unstated.

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 table, schema, columns and minCount are already documented in the schema; the baseline is 3. The description reinforces the minCount default and NULL exclusion but adds no format or syntax detail beyond what the schema already supplies.

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?

States a specific verb+resource ('Find duplicate value groups') and pins the scope ('in one or more columns of a table'), which is enough to distinguish it from adjacent siblings like count_rows or get_sample_data. An agent can tell what it returns without opening any schema.

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?

Usage is implied by the purpose (you call it when you suspect duplicates across given columns), but there is no explicit when-to-use, no when-not-to-use, and no routing to or from siblings such as validate_fk_integrity or count_rows. Adequate but leaves the agent to infer the trigger condition.

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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