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Glama

DataPulse MY

by io.github.r3dz4r

Summarize Aggregate Tool Usage

usage_summary
Read-onlyIdempotent

Aggregate anonymous tool usage for an inclusive ISO date range, e.g. 2026-08-01 to 2026-08-07. Returns total_calls, by_outcome, by_tool, by_dataset, trust_distribution (per-status counts of cited datasets) for the inclusive range. Legacy identity fields are ignored.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sinceYesInclusive ISO start date YYYY-MM-DD, e.g. '2026-08-01'.
untilYesInclusive ISO end date YYYY-MM-DD, e.g. '2026-08-07'.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

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

  1. Changed2 schema fields changed
    • removedInput schema / properties / buyer_id
      Removed value: -{
-  "description": "Buyer identifier, e.g. 'pro-default' or 'anonymous'.",
-  "examples": [
-    "pro-default"
-  ],
-  "minLength": 1,
-  "type": "string"
-}
    • changedInput schema / required
      Previous value: -[
-  "buyer_id",
-  "since",
-  "until"
-]New value: +[
+  "since",
+  "until"
+]
  2. Added

TDQS

A4.3/5.0
Behavior4/5

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

The annotations already mark the tool as readOnly, idempotent, and non-destructive. Beyond that, the description adds meaningful behavioral context: the data is anonymous, the date range is inclusive, legacy identity fields are ignored, and the response contains specific aggregate fields. This is valuable context not present in the annotations alone.

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 compact: two sentences that front-load the core purpose and date semantics, then list the returned fields. Every sentence earns its place, and the legacy identity note is a useful behavioral caveat with no wasted words.

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?

For a two-parameter read-only aggregation tool with a full output schema and rich annotations, the description is complete. It covers the operation, date inclusivity, anonymity, output fields, and the legacy-identity caveat, leaving no significant operational gap for an agent selecting or invoking the tool.

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

Parameters3/5

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

The schema already fully documents both parameters with descriptions and examples (100% coverage), so the baseline is 3. The description reinforces the inclusive ISO date range and gives a concrete example, but it does not add substantially new parameter-level meaning beyond what the schema already provides.

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 a specific verb ('aggregate'), a specific resource ('anonymous tool usage'), and a date-range scope. It also names the exact output fields, which distinguishes it from all sibling tools that deal with datasets or reconciliation rather than usage analytics.

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 establishes clear context for when to use this tool: when aggregate tool usage over an inclusive date range is needed. It does not explicitly mention alternatives or exclusion criteria, but no sibling tool appears to offer the same aggregate usage functionality, so the absence of explicit exclusions is acceptable.

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.

Resources