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AI-ML-Lab-MCP

AI-ML-Lab-MCP

by SyedHassan904
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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Duplicate Rows

duplicate_rows

Detects duplicate records in a dataset and returns the count of rows that repeat an earlier row.

Instructions

Detect duplicate records and report their count. Args: dataset_id (str): ID of the dataset. Returns: dict: duplicated_rows_count (rows that repeat an earlier row).

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": {
      "dataset_id": "<dataset_id>"
    },
    "name": "duplicate_rows"
  }
}

Input Schema

NameRequiredDescriptionDefault
dataset_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.9/5.0
Behavior3/5

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

No annotations are supplied, so the description carries the disclosure burden. It does add genuinely useful behavior semantics by defining the return key as 'rows that repeat an earlier row', which clarifies keep-first counting rather than counting every member of a duplicate group. It does not state read-only safety, permissions, or how missing values are treated, leaving meaningful gaps for an unannotated tool.

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

Conciseness3/5

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

The prose is short and front-loaded with the core purpose, but the Args/Returns docstring scaffolding adds boilerplate around a single parameter. The one line that clarifies counting semantics earns its place; the type restatement does not.

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

Completeness3/5

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

With no output schema, the description correctly supplies the return shape, which is the most important missing piece. However, for a tool with zero annotation coverage it should also say whether the dataset is modified and how duplicates are counted, so it is only marginally complete.

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

Parameters2/5

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

Schema description coverage is 0% for the single required parameter, so the description must compensate. It only repeats 'dataset_id (str): ID of the dataset', which adds no format, provenance, or expected-value guidance beyond the schema's own type declaration.

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?

States a specific verb and resource: 'Detect duplicate records and report their count.' An agent immediately knows this is a read-only duplicate-detection operation on a dataset. It doesn't explicitly differentiate from nearby siblings like detect_outliers or drop_missing, but the purpose itself is unambiguous.

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 when-to-use guidance at all: no statement of prerequisites, no mention of when to prefer it over row-dropping or other analysis tools, and no indication of what the agent should do with the count. Usage must be fully inferred from the name.

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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