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Glama

Statcan Cube Data

statcan_cube_data
Read-onlyIdempotent

Latest N observations for a specific series within a StatCan cube. coordinate is a 10-position dot-separated string indexing each dimension (map members → positions via statcan_cube_metadata). Trailing zeros for unused dimensions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
n_periodsNoLatest N periods (default 12).
coordinateYes10-position coordinate, e.g. "1.2.0.0.0.0.0.0.0.0".
product_idYesCube product ID.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
vector_idYesSeries vector ID
coordinateYes10-position coordinate string
product_idYesCube product ID
observationsYesLatest N observations
series_titleYesEnglish series title
frequency_codeYesRelease frequency code

Schema Changelog

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

  1. Added

TDQS

A3.9/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, covering safety. Description adds coordinate format context but no additional behavioral traits beyond what annotations provide. No contradictions.

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 concise sentences. First sentence states purpose, second explains coordinate usage. No unnecessary words. Front-loaded and efficient.

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?

Given annotations, output schema, and schema coverage, the description is complete. No gaps; explains coordinate and references metadata tool. Return values covered by output schema.

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?

Schema description coverage is 100%, so baseline is 3. Description repeats coordinate format and mentions trailing zeros, mirroring schema. No new semantics beyond schema for any parameter.

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?

Clearly states it retrieves the latest N observations for a specific series within a StatCan cube. Explains the coordinate parameter and references statcan_cube_metadata for dimension mapping, distinguishing it from siblings like statcan_series and statcan_cube_metadata.

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

Usage Guidelines3/5

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

Implicitly suggests use when coordinate and product_id are known, but lacks explicit guidance on when to prefer this tool over alternatives or when not to use it. No when-to-use or when-not-to-use statements.

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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TDQS

A3.8/5.0
Disambiguation2/5

Several tools are near-duplicates: ask_pipeworx_beta is explicitly identical to ask_pipeworx right now, ask_pipeworx_grounded is the same router with an extra extraction pass, and deep_research/ask_pipeworx overlap for broad questions. The prediction-market tools and the StatCan series/cube/indicator tools also have fuzzy boundaries despite their detailed descriptions.

Naming Consistency3/5

Names consistently use snake_case, but the set mixes verb-first names (resolve_entity, validate_claim, subscribe) with domain-prefixed noun-first names (statcan_*, polymarket_*, pipeworx_*) and one-off names like ai_visibility_check and generate_llms_txt. The domain prefixes help navigation, but there is no single predictable pattern and the ask_pipeworx_* suffix variants break the prefix convention.

Tool Count2/5

38 tools is well beyond the 25+ threshold, and the server named Statcan carries only 8 StatCan-specific tools alongside general Pipeworx routing, prediction-market analysis, AI visibility, dependency scanning, memory, and subscription features. This feels like several servers merged into one rather than a well-scoped StatCan interface.

Completeness4/5

Within the apparent StatCan data-access scope, the surface is solid: listing cubes, metadata, cube data, vector series, headline indicators, CSV URLs, and change detection cover the core workflows. The broader Pipeworx/analysis layers also include discovery, grounded lookups, entity resolution, validation, subscriptions, and memory, with only minor gaps like server-side StatCan search and no way to execute on prediction-market signals.