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Statcan Cube Metadata

statcan_cube_metadata
Read-onlyIdempotent

Full metadata for a StatCan cube: dimensions, member trees, frequency, geography, last release. Use it to construct a coordinate string for statcan_cube_data. Does NOT return vector IDs — for that use statcan_cube_vectors, which enumerates the actual vector id for every dimension-member combination in the cube.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
product_idYesCube product ID (8-digit, e.g. 36100434 = quarterly GDP).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
end_dateYesCube end date
title_enYesEnglish cube title
title_frYesFrench cube title
cansim_idYesCANSIM identifier
dimensionsYesCube dimensions
product_idYesCube product ID
start_dateYesCube start date
series_countYesTotal series in cube
archive_statusYesArchive status in English
frequency_codeYesRelease frequency code
datapoint_countYesTotal datapoints in cube

Schema Changelog

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

  1. Added

TDQS

A4.5/5.0
Behavior4/5

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

Adds the negative behavior 'Does NOT return vector IDs' beyond the annotations, preventing a common mistaken use. Annotations already establish read-only, idempotent, open-world safety profile, so the description only needs to add behavioral specifics.

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?

Three sentences, each with a distinct purpose: what it returns, how to use it, and what it doesn't return with a pointer to the sibling. No 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?

For a single-parameter read-only metadata tool with an output schema present, the description covers purpose, usage, and key exclusions. Nothing an agent needs to select and call it correctly is missing.

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 documents product_id fully with type, description and example (100% coverage). The description adds no additional parameter-level meaning, so the baseline score of 3 applies.

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?

States a specific verb (returns) and resource (StatCan cube metadata) with enumerated contents (dimensions, member trees, frequency, geography, last release). Explicitly distinguishes itself from statcan_cube_vectors by stating what it does not return, making it easy to differentiate.

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?

Gives explicit use case: construct a coordinate string for statcan_cube_data. Names the alternative statcan_cube_vectors for when vector IDs are needed, providing a clear when-to-use/when-not-to-use distinction.

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.