APIbase

Statistics Canada — Series Info

statcan.series.info

Read-onlyIdempotent

Retrieve Statistics Canada time-series metadata by vector ID to get title, parent table, coordinates, release frequency, decimal precision, and termination status before fetching observations.

Instructions

Get metadata for a specific Statistics Canada time-series (vector) by its vector ID. Returns the series title (e.g. "Ontario; Consumer Price Index, All items"), parent table product ID, coordinate within the table, release frequency, decimal precision, and whether the series is terminated. Vector IDs are 8–9 digit numbers assigned by Statistics Canada to uniquely identify each time series. Use series_data to retrieve actual observations. Source: Statistics Canada WDS, Canada Open Licence, no auth required.

Input Schema

Name	Required	Description	Default
`vector_id`	Yes	Statistics Canada vector ID identifying a specific time series within a table (e.g. 32164132). Obtain from table_metadata dimensions or published dataset documentation.

Output Schema

Name	Required	Description	Default
`error`	No	Present only when the call failed. Includes error code, message, request_id, and any provider-specific extras.
`result`	No	Tool response payload. Shape varies per tool — consult the tool description and inputSchema. May be an object, array, string, or number depending on the upstream provider response.

Schema Changelog

Changes observed during successful MCP inspections.

Addedv2.2.0Oct 29, 2026

TDQS

A4.7/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds useful non-schema context: 'no auth required', the data source (Statistics Canada WDS), licensing (Canada Open Licence), and the exact set of metadata fields returned. These go beyond the structured fields.

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 and a source tag, with the core action and payoff front-loaded in the first sentence. Every sentence earns its place—purpose, ID format, sibling routing, and provenance—with zero 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 lookup with an output schema, the description is complete: it specifies what is returned, what the vector ID means and how to obtain it, when to use the sibling data tool, and the auth/licensing situation. Nothing essential is missing.

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?

Schema description coverage is 100% and already includes an example and guidance on how to obtain the vector_id. The description adds extra semantic value by explaining that vector IDs are '8–9 digit numbers assigned by Statistics Canada to uniquely identify each time series,' which enriches the agent's understanding of the input.

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 ('Get metadata'), a resource ('a specific Statistics Canada time-series'), and the lookup key ('by its vector ID'). It also enumerates the returned fields and explicitly distinguishes itself from the sibling statcan.series.data: 'Use series_data to retrieve actual observations.' An agent can tell them apart immediately.

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 when-not guidance with a named alternative: 'Use series_data to retrieve actual observations.' It also explains how to obtain the required vector_id (from table_metadata dimensions or published dataset documentation), which is actionable usage context for a single-parameter tool.

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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