Spain Open Data MCP

Price range for a place

price_band

Read-onlyIdempotent

Adjust a known price to a Spanish place using local CPI and median income, then return an estimated price range for today.

Instructions

Suggest a price range for a place from local purchasing power and inflation.

Public data has no competitor prices, so this adjusts a price you know (from elsewhere or
an earlier date) to this place today: CPI for the category's group since that month, then
local median income per consumption unit against the reference place. Returns a range
from no local adjustment to full adjustment, labelled as an estimate.

Input 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.
`category`	No	Market category, to uprate with its own CPI group (e.g. 'gym', 'restaurant').
`price_month`	No	Month the reference price applied, YYYY-MM, to uprate it to today.
`reference_place`	No	Where the reference price applies: 'ES' (Spain, default) or a place.	ES
`reference_price`	Yes	Price elsewhere, in euros, e.g. 39.9 for a monthly gym fee.

Output 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.

Addedv0.1.1Oct 17, 2026

TDQS

A3.9/5.0

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), and the description adds meaningful context beyond that: the two-stage adjustment mechanism, the fact that no competitor price data exists, and that output is a range 'labelled as an estimate'. It does not discuss pagination, latency, or failure modes, but for a read-only computed estimate those are minor.

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 purpose and followed by the rationale and the return shape. Well-structured and mostly waste-free, though the second sentence is dense enough that a reader must work through it.

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, the description need not detail return fields, and it still supplies the key epistemic caveat (estimate, range from no adjustment to full adjustment) plus the data limitation. Combined with full schema coverage and clear annotations, an agent has what it needs to call it correctly.

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%, so the schema already defines every parameter including place formats and defaults. The description adds only the conceptual role of price_month and reference_place (uprating vs. comparison) which the schema largely conveys; baseline 3 applies.

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?

States a specific verb and resource: 'Suggest a price range for a place' from purchasing power and inflation, with the method (CPI uprating plus median income per consumption unit) spelled out. It does not, however, contrast itself with siblings such as rents, income, or market_size, so an agent must infer the boundary.

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?

Gives a clear usage context: it applies when public data has no competitor prices, so you supply a price you already know 'from elsewhere or an earlier date' and it adjusts it to the place today. No explicit exclusions or named alternative tools are provided, 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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