Spain Open Data MCP

Population and households

demographics

Read-onlyIdempotent

Retrieve population, age, sex, household, and trend data for any Spanish place, from municipalities to census tracts, cross-checked with INE data.

Instructions

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. 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

Name	Required	Description	Default
`place`	Yes	Place 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_areas`	No	For a municipality, also list its 'district's or census 'tract's. Default 'none'.	none

Output Schema

TableJSON Schema

Name	Required	Description
`data`	Yes
`checks`	No
`sources`	Yes
`confidence`	Yes	Lowest confidence across inputs: high, medium or low. Never state a conclusion more strongly than this.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Oct 16, 2026

TDQS

A4/5.0

Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive, open-world behavior, so the bar is lower. The description still adds real context: the underlying sources (census cross-checked against the official Padrón, INE income atlas) and the vintage ('latest 1 January'), which tells the agent how fresh and authoritative the data is.

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?

Three sentences, front-loaded with the returned data and free of filler; the level-by-level breakdown is dense but each clause carries information. It is slightly heavy for the small 2-parameter surface but not padded.

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 formatting need not be explained, and the description covers data content, sources, granularity, and a comparison workflow. The only omission is any hint about ambiguous-name handling or relation to resolve_place, which the schema partially mitigates with the 'Name, Province' convention.

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 baseline is 3. The description goes beyond the schema by mapping each geographic level to the data it yields (census age bands at municipality/province/CCAA vs. household metrics at district/tract) and by illustrating sub_areas' comparison purpose, adding meaning the enum values alone do not convey.

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 enumerates exactly what is returned (population, sex, five-year age bands, 5-year trend, household profile) and constrains it to a place in Spain, so the resource is unambiguous. It does not explicitly name or contrast with its siblings (resolve_place, income), so it falls 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 a concrete use case for sub_areas ('compare a city's districts or tracts... to find where young families or older residents concentrate') and explains which data is available at which geographic level. It does not state when to prefer resolve_place for name disambiguation or when income would be the better tool, so usage context is clear but not exhaustive.

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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