statcan-mcp

Get time series by table coordinate

get_data_by_coordinate

Retrieve data observations for any Statistics Canada table by selecting one member ID for each dimension, with optional limit on most recent periods.

Instructions

Fetch observations by choosing one member ID per dimension of a table (get them from get_table_metadata). Example: table 18100004 with member_ids [2, 2] = CPI, Canada, all-items.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "product_id": "<product_id>",
      "member_ids": "<member_ids>"
    },
    "name": "get_data_by_coordinate"
  }
}

Input Schema

Name	Required	Description
`latest_n`	No	N most recent periods
`member_ids`	Yes	One member ID per dimension, in dimension order
`product_id`	Yes	Table product ID, e.g. 18100004

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.1Aug 17, 2026

TDQS

A3.7/5.0

Behavior2/5

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

No annotations are provided, so the description bears full responsibility for behavioral disclosure. It says 'Fetch observations' (implying read-only) but does not describe the return structure, format, error behavior, or any side effects. This minimal transparency is a significant gap for a tool with no output schema.

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 exactly two sentences: the first states the purpose concisely, the second provides a illustrative example. No superfluous information, front-loaded with the primary verb, and easy to scan.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With three parameters and no output schema, the description covers the core concept and includes a helpful example, but it omits what the returned observations look like. It is reasonably complete for understanding how to invoke the tool, yet the absence of any description of the response structure leaves a gap.

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?

All three parameters have schema descriptions, giving 100% coverage. The description adds an example that clarifies the semantics of member_ids, but it does not materially enhance understanding beyond the schema, so the baseline score of 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?

Description clearly states the action ('Fetch observations') and the resource ('table by coordinate'), with a concrete example (table 18100004, member_ids [2,2] = CPI, Canada, all-items). It also references get_table_metadata, distinguishing it from sibling get_data_by_vectors which likely uses a different query method.

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?

Provides clear context: users must obtain member IDs from get_table_metadata, and the example demonstrates correct usage. However, it does not explicitly contrast with alternatives like get_data_by_vectors or state exclusions, so it falls short of a full 'when to use vs. not' guidance.

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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