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Population and households

demographics
Read-onlyIdempotent

Population, sex, five-year age bands, 5-year trend, and household profile for a place in Spain.

Municipality, province and CCAA: census population by age band (latest 1 January),
cross-checked against the official Padrón figure. District and tract: census age bands
and sex from INE's file by census tract. Municipality, district and tract:
household size, single-person households, mean age, share under 18 and 65+ from INE's
income atlas. Use sub_areas to compare a city's districts or tracts, e.g. to find
where young families or older residents concentrate.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
placeYesPlace name or INE code: municipality ('Valladolid' or '47186'), province ('41'; a bare name like 'Sevilla' means the city), autonomous community ('Cataluña' or 'CA09'), district ('4718603') or census tract ('4718603012'). 'Name, Province' picks between same-named towns.
sub_areasNoFor a municipality, also list its 'district's or census 'tract's. Default 'none'.none

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataYes
checksNo
sourcesYes
confidenceYesLowest confidence across inputs: high, medium or low. Never state a conclusion more strongly than this.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed4 schema fields changed
    • addedOutput schema / $defs / Place / properties / area_names
      Added value: +{
      +  "anyOf": [
      +    {
      +      "type": "string"
      +    },
      +    {
      +      "type": "null"
      +    }
      +  ],
      +  "default": null,
      +  "description": "For districts and tracts: OpenStreetMap neighbourhood names inside it (not official INE names).",
      +  "title": "Area Names"
      +}
    • changedOutput schema / $defs / Place / properties / match / description
      Previous value: -"How the query matched. 'fuzzy' means a spelling guess: confirm it."New value: +"How the query matched. 'fuzzy' means a spelling guess: confirm it. 'neighbourhood' means a neighbourhood name was found inside this INE district."
    • changedOutput schema / $defs / Place / properties / match / enum
      Previous value: -[
      -  "code",
      -  "exact",
      -  "prefix",
      -  "fuzzy"
      -]New value: +[
      +  "code",
      +  "exact",
      +  "prefix",
      +  "fuzzy",
      +  "neighbourhood"
      +]
    • addedOutput schema / $defs / Place / properties / matched_name
      Added value: +{
      +  "anyOf": [
      +    {
      +      "type": "string"
      +    },
      +    {
      +      "type": "null"
      +    }
      +  ],
      +  "default": null,
      +  "description": "The neighbourhood name that matched, if any.",
      +  "title": "Matched Name"
      +}
  2. First observed

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, openWorld and non-destructive, so safety is covered. The description adds real behavioral context beyond that: it discloses the data sources (INE census, official Padrón, INE income atlas) and, critically, that the returned variables differ by place granularity (municipality/province/CCAA vs district/tract), which is not derivable from the annotations.

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 opening sentence front-loads the resource set, and the body is organized by geographic level. It is slightly repetitive in restating 'Municipality, district and tract', but each line carries distinct content about which metrics apply where.

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

Completeness4/5

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

With an output schema present, return-value explanation is unnecessary, and the description covers scope, sources, granularity-dependent outputs and the sub_areas use case. It is largely complete for a two-parameter read tool, with only sibling-level differentiation left implicit.

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 coverage is 100%, so the baseline is 3. The description goes further by tying place granularity to which metrics come back and by explaining the practical intent of sub_areas, adding meaning beyond the enum/default already in the schema.

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 names the concrete resources returned (population, sex, five-year age bands, 5-year trend, household size, single-person households, mean age, share under 18/65+) and the geographic scope (a place in Spain), so an agent knows exactly what data this yields. It does not, however, explicitly contrast itself with adjacent siblings such as income, rents or crime, so it stops short of full sibling differentiation.

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?

It gives clear situational guidance for the sub_areas parameter ('Use sub_areas to compare a city's districts or tracts, e.g. to find where young families or older residents concentrate'), which tells the agent when to reach for sub-area breakdowns. There is no explicit when-not guidance or naming of alternative sibling tools (e.g. income or rents) for related questions.

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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