Spain Open Data MCP

Household income

income

Read-onlyIdempotent

Retrieve household earnings for Spanish places from INE's atlas down to census tract, with net/gross, inequality, sources, trends, and benchmarks. Use for purchasing power and district comparisons.

Instructions

Household income for a place in Spain from INE's income atlas, down to census tract.

Returns net and gross income per person and per household, mean and median per consumption unit, inequality (Gini, P80/P20), income sources (wages, pensions, benefits), a trend, and benchmarks against the province, region and Spain. Use it for purchasing power, price positioning and choosing between districts. Figures are euros of the data year; the atlas runs about two years behind.

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.
`years`	No	Years of history for the trend (max 9).
`rank_by`	No	Metric used to rank sub-areas.	net_income_per_person
`sub_areas`	No	For a municipality, rank 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

A4.2/5.0

Behavior4/5

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, openWorld), so the description is free to add the genuinely useful operational facts: figures are in euros of the data year and the atlas lags about two years. That data-freshness caveat is exactly the kind of context annotations cannot express. Auth/rate-limit behavior is not covered, keeping it below 5.

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?

Front-loaded with the resource and scope, then use cases and the freshness caveat. The middle sentence enumerates return values fairly exhaustively, which is partly redundant given an output schema exists, but there is no filler or repetition.

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?

With an output schema, full parameter documentation and rich annotations, the only missing pieces an agent would need are units and data vintage, both of which are supplied. Nothing required to call the tool correctly is absent.

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?

Schema description coverage is 100%, including enum values and the 'Name, Province' disambiguation rule, so the schema carries the parameter burden. The description adds only indirect hints (census tract, trend, ranking districts) and no syntax or default guidance beyond that. Baseline 3 applies.

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?

States a specific resource (INE household income for a place in Spain) and its full granularity, down to census tract, plus the exact metric families returned. An agent can distinguish this from demographics (population) and resolve_place (name resolution) without opening either schema.

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?

Explicit use cases are given: purchasing power, price positioning, choosing between districts. There are no stated exclusions or named alternatives (e.g. when to prefer demographics or to resolve_place first), so it stops short of a 5.

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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