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Glama

Statcan

by io.github.pipeworx-io

Statcan Cube Vectors

statcan_cube_vectors
Read-onlyIdempotent

Enumerate the vector IDs inside a StatCan cube (table) — the missing link between a table number/name (e.g. "32-10-0121-01" or "Production and disposition of eggs") and statcan_series, which needs a specific vector id. Every StatCan question starts with a table, but nothing else in this pack lists a table's vectors. Returns one row per dimension-member combination: vector_id, coordinate, and the member label for every dimension (e.g. GEO=Canada, Production and disposition=Average number of layers). Filter by dimension name/value (e.g. {"GEO":"Canada"}) to narrow a large table — unfiltered dimensions include ALL their members, which can be thousands of rows, so use filters plus limit/page. Then pass any returned vector_id to statcan_series for the actual data.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNoPage number, 1-based (default 1).
limitNoMax vector rows per page (default 100, max 500).
filtersNoOptional dimension filters, e.g. {"GEO": "Canada"} or {"Geography": "British Columbia"}. Keys match by case-insensitive substring against the cube's dimension names (so "GEO" matches "Geography"); values match by case-insensitive substring against member names. Dimensions with no filter include every member.
product_idYesCube product ID, either form: "32100121" or the table number "32-10-0121-01".

Schema Changelog

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

  1. Added

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, and the description adds valuable behavioral context beyond that: it discloses that unfiltered dimensions include ALL members, can return thousands of rows, and describes the exact row shape (vector_id, coordinate, member labels). This gives the agent realistic expectations about scale and output.

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 and every sentence earns its place: purpose, output shape, filtering risk, and next-step workflow. It is front-loaded with the primary purpose and structured so an agent can quickly decide whether to invoke it.

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?

Even without an output schema, the description explains what rows are returned, warns about large result sets, mentions pagination/filtering, and connects the tool to statcan_series. This fully covers an agent's need to select and invoke the tool correctly within the StatCan workflow.

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 the input schema already explains product_id forms, filters, page, and limit. The description reinforces filtering behavior and warns about unfiltered dimensions, but it does not add materially new parameter semantics beyond what the schema already provides. Baseline 3 is appropriate.

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 verb and resource: 'Enumerate the vector IDs inside a StatCan cube (table)'. It also explicitly positions the tool as the 'missing link' between a table number/name and statcan_series, which distinguishes it from sibling tools like statcan_series and 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 Guidelines5/5

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

The description gives explicit usage context: 'Every StatCan question starts with a table, but nothing else in this pack lists a table's vectors.' It also provides a clear workflow by instructing the agent to pass a returned vector_id to statcan_series for actual data, making the when-to-use and next-step guidance unambiguous.

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.

Resources