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Glama

Statcan

by io.github.pipeworx-io

Statcan List Cubes

statcan_list_cubes
Read-onlyIdempotent

List all available StatCan cubes (tables) — lean: productId + title (en/fr) + CANSIM id + dimension count + release date. Use the productId with statcan_cube_metadata / statcan_cube_data. Response is large (~3,000 cubes); filter client-side.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
countYesTotal number of cubes
cubesYesList of available cubes

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?

Annotations already declare readOnly, openWorld, idempotent, non-destructive. Description adds value by explaining the response contains a lean set of fields and is large (~3,000 cubes), which informs agent behavior.

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, front-loaded with purpose. No wasted words; includes essential context (fields, size, downstream use).

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 zero-parameter tool with rich annotations and output schema, the description fully explains what it returns, its size, and how to use the output. No missing critical information.

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?

No parameters; description correctly indicates no input needed. Schema coverage is 100% (no params to describe), so baseline 4 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?

Clearly states it lists all StatCan cubes with specific fields (productId, title, CANSIM id, etc.). Distinguishes from siblings by directing to use productId with statcan_cube_metadata/statcan_cube_data.

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?

Explicitly tells to use productId from this tool with other tools, and notes the large response size requiring client-side filtering. Implicitly suggests when not to use (when needing full metadata/data).

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.

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