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DriftScope

DriftScope

by Aymenrahmanii
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

generate_mock_datasets

Generates synthetic baseline and production CSV files to test data drift detection and model degradation in tabular ML pipelines.

Instructions

Generates synthetic baseline and production CSVs in the local cloud storage.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "generate_mock_datasets"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.2/5.0
Behavior2/5

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

No annotations exist, so the description carries the full burden. It does hint at a write side effect by naming a storage destination, but it never states whether existing files are overwritten, what permissions are needed, how large the generated data is, or whether the operation is repeatable. For a mutating generator this is a significant gap.

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?

A single front-loaded sentence with no filler; every word (synthetic, baseline, production, CSVs, storage) contributes to the meaning.

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 and no annotations, the description should explain what gets written and what is returned, but 'local cloud storage' remains vague and the difference between the baseline and production outputs is never characterized. Adequate but leaves real ambiguity for a side-effecting tool.

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

Parameters4/5

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

The schema has zero parameters, so there is nothing for the description to disambiguate. Baseline 4 applies per the rubric.

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?

Specific verb ('Generates') plus a concrete resource ('synthetic baseline and production CSVs') with a stated destination. It is unambiguous what the tool produces, though it does not explicitly contrast itself with the sibling monitoring tools (check_feature_drift, compute_psi).

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 gives no indication of when to use this tool, no prerequisites, and no conditions that would select it over the sibling tools. An agent must infer that this is a fixture/setup step from the word 'mock' alone.

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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