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find_duplicates

Identify and handle duplicate rows in Excel/CSV files using configurable strategies including highlighting, removal, or exporting duplicates.

Instructions

Find and manage duplicate rows in Excel/CSV files with multiple strategies

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": {
      "filePath": "<filePath>"
    },
    "name": "find_duplicates"
  }
}

Input Schema

Name	Required	Description
`filePath`	Yes	Path to the CSV or Excel file
`columns`	No	Columns to check for duplicates (empty = all columns)
`action`	No	What to do with duplicates (default: report_only)
`keepFirst`	No	Keep first occurrence when removing (default: true)
`sheet`	No	Sheet name for Excel files (optional)

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Jan 2, 2026

TDQS

C2.9/5.0

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. It mentions 'multiple strategies' but doesn't specify what they are, how duplicates are identified (e.g., exact matches, fuzzy logic), or the tool's behavior (e.g., whether it modifies files in-place, creates new files, or requires specific permissions). This leaves significant gaps for a tool that performs file operations.

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, efficient sentence that front-loads the core purpose. It avoids unnecessary words, though it could be slightly more structured by explicitly listing the strategies or use cases to enhance clarity without sacrificing brevity.

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?

Given the complexity (file operations with multiple actions), lack of annotations, and no output schema, the description is incomplete. It doesn't cover behavioral aspects like file modification effects, error handling, or output format, which are critical for safe and effective tool invocation by an AI agent.

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 documents all parameters thoroughly. The description adds no additional meaning beyond what's in the schema, such as explaining the 'multiple strategies' mentioned or providing examples. Baseline 3 is appropriate when the schema does the heavy lifting.

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 clearly states the verb ('find and manage') and resource ('duplicate rows in Excel/CSV files'), distinguishing it from siblings like data_cleaner or validate_data_consistency. However, it doesn't explicitly differentiate from all possible alternatives, such as filter_rows or search, which could also handle duplicates in some contexts.

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?

The description provides no guidance on when to use this tool versus alternatives. With many sibling tools like data_cleaner, filter_rows, and search, there's no indication of specific scenarios, prerequisites, or exclusions for choosing find_duplicates over other options.

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