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malkreide

swiss-statistics-mcp

by malkreide

bfs_population

Read-onlyIdempotent

Retrieve Swiss population figures by region, year, age, or gender. Query demographic data for school planning and projections.

Instructions

Retrieve Swiss population statistics by region, year, and breakdown.

Accesses the core BFS population dataset (ständige Wohnbevölkerung) with flexible filtering by canton/municipality, year, age, and gender. Critical for school space planning and demographic projections.

Args: params (GetPopulationInput): - region (str): 'Schweiz', or canton name like 'Zürich' - year (Optional[str]): Year filter, e.g. '2024' - breakdown (str): 'total', 'age', or 'gender'

Returns: str: JSON with population figures for the selected region and breakdown.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paramsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
errorNo
hintNo
table_idNo
titleNo
sourceNo
updatedNo
languageNo
dimensionsNo
rows_totalNo
rows_returnedNo
truncatedNo
rowsNo
noteNo
cantons_comparedNo
canton_variableNo
topicNo
topic_descriptionNo
cantonNo
canton_filterNo
regionNo
breakdownNo
yearNo
Behavior3/5

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

Annotations already indicate readOnlyHint, destructiveHint, idempotentHint. Description adds context about 'flexible filtering' and dataset source, but does not disclose behavioral traits beyond what annotations provide. No contradiction.

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?

Description is front-loaded with main purpose, followed by details in a structured Args/Returns format. Every sentence adds value; no unnecessary words.

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?

Given the tool has an output schema and the description covers parameters, return type, and use case, it is complete for an agent to select and invoke correctly.

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?

Despite schema description coverage of 0%, the description includes an Args section that explains each parameter (region, year, breakdown) with types and examples. This compensates for the missing schema descriptions, though it could be more detailed on year format.

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 it retrieves Swiss population statistics by region, year, and breakdown. It mentions the specific dataset and use cases (school space planning), distinguishing it from siblings like bfs_compare_cantons or bfs_education_stats.

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

Usage Guidelines3/5

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

Description implies usage for demographic projections, but does not explicitly state when not to use or compare to alternatives like bfs_compare_cantons. No exclusions or conditions provided.

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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