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mendrift-mcp

mendrift-mcp

by suneel190700
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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get_drift_report

Detect data drift in deployed models by comparing current and reference windows. Returns per-feature drift scores, overall flag, and top drifted features using PSI and KS tests.

Instructions

Compute a data/prediction drift report for a deployed model.

Compares the current serving window against a reference window (PSI + KS
tests per feature). Returns per-feature drift scores, an overall drift
flag, and the top drifted features.

Args:
    model_name: Registered model name as it appears in the model registry.
    reference_window_hours: Reference window size (default 7 days).
    current_window_hours: Current window size (default 24 hours).

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": {
      "model_name": "<model_name>"
    },
    "name": "get_drift_report"
  }
}

Input Schema

NameRequiredDescriptionDefault
model_nameYes
current_window_hoursNo
reference_window_hoursNo

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.2

TDQS

A4.3/5.0
Behavior3/5

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

With no annotations, the description is the sole source of behavioral disclosure. It explains that it computes a report and returns drift metrics, but does not disclose potential side effects, permissions, rate limits, or performance implications. The read-only nature is implied but not explicit, and there is no mention of prerequisites beyond a deployed model.

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?

The description is concise, front-loaded with a clear summary, and uses a simple Args list for parameters. Every sentence adds value, with no wasted words.

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?

The tool has an output schema, so return values need not be repeated. The description covers purpose, method, and all parameters adequately. Minor gaps like usage guidance relative to siblings and explicit behavioral traits prevent a perfect score.

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

Parameters5/5

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

Schema description coverage is 0%, so the description fully compensates by providing meaningful explanations for all three parameters. It defines model_name as the registry name, and gives both default values and units for the window sizes, adding clarity beyond the raw schema.

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?

The description clearly states it computes a data/prediction drift report, comparing serving windows with PSI + KS tests per feature. It also lists specific return outputs (per-feature scores, overall drift flag, top drifted features), distinguishing it from sibling tools like rollback or incident management.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description specifies the context (deployed model drift detection) and compares current vs reference windows, making clear when it is relevant. However, it does not explicitly mention alternatives or exclusions, so it falls short of a 5.

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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