dutch-gov-mcp

get_cbs_dimensions

Retrieve dimension definitions for CBS datasets to understand column meanings and permissible values, enabling data analysis without authentication.

Instructions

Get dimension definitions for a CBS dataset.

Dimensions describe what the columns mean and which values they can take.

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": {
      "dataset_id": "<dataset_id>"
    },
    "name": "get_cbs_dimensions"
  }
}

Input Schema

Name	Required	Description	Default
`dataset_id`	Yes	CBS dataset identifier (e.g. "83765NED").

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.3.0Apr 16, 2026

TDQS

C2.9/5.0

Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states what the tool does but lacks critical behavioral details: it doesn't specify if this is a read-only operation (implied by 'Get' but not explicit), what the output format is (though an output schema exists), error conditions, rate limits, or authentication needs. For a tool with no annotations, this leaves significant gaps in understanding how it behaves.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized and front-loaded: the first sentence states the core purpose, and the second adds clarifying context about dimensions. Both sentences earn their place by defining the tool and explaining 'dimensions', with no wasted words. However, it could be slightly more structured (e.g., by explicitly noting it's a read operation), preventing a perfect score.

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?

Given the complexity (a simple lookup tool with 1 parameter), high schema coverage (100%), and presence of an output schema, the description is somewhat complete but has gaps. It covers the basic purpose but lacks usage guidelines and behavioral details (e.g., error handling). The output schema mitigates the need to explain return values, but without annotations, the description should do more to compensate, making it only adequate.

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 description adds minimal meaning beyond the input schema. It mentions 'CBS dataset' and 'dimension definitions', which contextualizes the 'dataset_id' parameter, but the schema already has 100% coverage with a clear description ('CBS dataset identifier'). Since schema_description_coverage is high (>80%), the baseline is 3, and the description doesn't significantly enhance parameter understanding (e.g., it doesn't explain what a 'dimension' entails in detail).

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Get dimension definitions for a CBS dataset.' It specifies the verb ('Get') and resource ('dimension definitions'), and distinguishes it from siblings like 'get_cbs_dataset' (which likely retrieves dataset metadata) or 'query_cbs_data' (which likely queries actual data). However, it doesn't explicitly differentiate from 'get_dataset' or 'search_cbs_datasets', which might also involve dataset-related operations, keeping it from a perfect score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing a dataset ID from another tool), exclusions, or comparisons to siblings like 'get_cbs_dataset' or 'search_cbs_datasets'. The agent must infer usage from the purpose alone, which is insufficient for optimal tool selection.

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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