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r3dz4r

datapulse-my

by r3dz4r
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Identify Schema and Content Drift

find_schema_drift
Read-onlyIdempotent

Find datasets with structural or record-count drift, ranked by structural changes first. Filter by minimum change count to surface significant shape or column transitions.

Instructions

Return datasets with published structural or record-count drift evidence, ranked with structural changes first. Optionally require a minimum number of structural transitions; includes pipeline-computed evidence so agents do not infer drift from freshness alone. Use it for structural or record-count changes; do not use it for freshness risk, anomalies, trends, or reliability—use find_stale, find_anomalies, find_deteriorating, or find_unreliable instead. It reads precomputed drift data, so an empty result means no published drift row survives the selected filters; DataPulse is read-only, requires no API key, and the edge limits clients to roughly one request per second with a small burst, so pace or retry.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum structural-first ranked drift results to return, e.g. 50; omit it to use 50 without changing ranking.
min_change_countNoInclusive filter on the larger published shape or column transition count, e.g. 1; omit it to include every drift row.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv3.36.1
    • changedInput schema / properties / limit / description
      Previous value: -"Maximum ranked drift results to return; integer from 1 to 200, e.g. 50."New value: +"Maximum structural-first ranked drift results to return, e.g. 50; omit it to use 50 without changing ranking."
    • changedInput schema / properties / min_change_count / description
      Previous value: -"Minimum structural fingerprint or column-count transitions; integer from 0 to 100, e.g. 1."New value: +"Inclusive filter on the larger published shape or column transition count, e.g. 1; omit it to include every drift row."
  2. Addedv1.0.2

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already declare read-only, idempotent, and non-destructive behavior, but the description adds valuable operational context: it reads precomputed drift data, explains that an empty result means no published drift row survives filters, notes DataPulse requires no API key, and warns about the rate limit with pacing guidance. No contradiction with annotations.

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 dense but every clause earns its place: core behavior, parameter nuance, usage boundaries, empty-result interpretation, auth/rate-limit context, and alternative tools. It front-loads the primary purpose and avoids filler.

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

Completeness5/5

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

The description covers purpose, parameter semantics, behavioral caveats, rate limits, and sibling routing. With an output schema present, the return shape is already documented, so no critical information is missing for an agent to select and invoke the tool correctly.

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?

Schema coverage is 100%, so the baseline is 3. The description adds meaningful nuance by framing min_change_count as 'a minimum number of structural transitions' and reinforcing that results are ranked structural-first, which goes slightly beyond the schema's raw parameter descriptions.

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 opens with a specific verb and resource: 'Return datasets with published structural or record-count drift evidence, ranked with structural changes first.' It clearly distinguishes the tool from siblings by naming the exact purpose (drift detection) and later lists what it is not for.

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

Usage Guidelines5/5

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

The description explicitly states when to use the tool and when not to: 'Use it for structural or record-count changes; do not use it for freshness risk, anomalies, trends, or reliability—use find_stale, find_anomalies, find_deteriorating, or find_unreliable instead.' This is model guidance with named alternatives.

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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