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Glama

DataPulse MY

by io.github.r3dz4r

Identify Schema and Content Drift

find_schema_drift
Read-onlyIdempotent

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.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum ranked drift results to return; integer from 1 to 200, e.g. 50.
min_change_countNoMinimum structural fingerprint or column-count transitions; integer from 0 to 100, e.g. 1.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A4.7/5.0
Behavior4/5

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

Annotations already cover safety (readOnly, idempotent, non-destructive). The description adds meaningful behavior details: ranking order (structural changes first), optional minimum transition threshold, and the inclusion of pipeline-computed evidence. It goes beyond simple read-only scoping.

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?

Two sentences with no wasted words. The main purpose is front-loaded, and each clause adds crucial context (ranking, optional filter, evidence provenance) without unnecessary detail.

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?

Despite being short, the description fully covers the tool's purpose, ranking behavior, key parameter, and a usage caveat. Given the output schema and strong annotations, no critical information is missing for an agent to select and invoke this 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 description coverage is 100%, so the baseline is 3. The description adds value by explaining that min_change_count requires 'a minimum number of structural transitions' and clarifies the ranking behavior, which complements the parameter 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 the tool returns datasets with structural or record-count drift evidence, ranked with structural changes first. This specific verb+resource+scope distinguishes it from sibling tools like find_stale and find_anomalies.

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 provides an explicit when-not directive: 'so agents do not infer drift from freshness alone'. This tells agents this tool should be used when pipeline-computed drift evidence is needed, setting it apart from freshness-based approaches.

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

All DataPulse MY toolsInspect
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TDQS

A4.4/5.0
Disambiguation4/5

Each tool targets a distinct workflow—search, detail retrieval, freshness checking, licence enumeration, and citation metadata. The only mild overlap is between get_dataset and get_provenance, both exposing metadata, but their descriptions differentiate full health/freshness detail from citation-ready provenance.

Naming Consistency5/5

All tool names follow a clear verb_noun pattern in snake_case: find_, get_, and search_ prefixes are used consistently. Minor stylistic variation between find_by_licence and find_stale does not undermine predictability.

Tool Count5/5

Five tools is well-scoped for a dataset catalog server, covering discovery, inspection, health assessment, licence scoping, and citation. No redundant or excessive tools are present.

Completeness4/5

The core lifecycle is covered: search to find datasets, get_dataset for full detail, find_stale for freshness risk, and get_provenance for citation. A minor gap is the lack of a general list-all or status filter beyond stale, but the domain is narrow enough to work around this.

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