Spain Open Data MCP

Find open datasets

find_datasets

Read-onlyIdempotent

Search Spain's national open-data catalogue by topic to find datasets with titles, publishers, licences, formats, update frequency and IDs. Use for Spanish data like parking, transport or tourism.

Instructions

Search Spain's national open-data catalogue (datos.gob.es) for datasets on a topic.

Returns each dataset's title, publisher, licence, file formats, update frequency and last
change, and an id to pass to read_dataset. Use when a question needs data this server does
not hold directly (parking, licences, local events, tourist offices, transport).

Input Schema

Name	Required	Description
`limit`	No	How many datasets to return.
`place`	No	Optional place; datasets about it are preferred.
`query`	Yes	Topic; Spanish works best, e.g. 'turismo', 'aparcamiento', 'licencias'.

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

A4.2/5.0

Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint and destructiveHint=false, so the safety profile is covered. The description adds the fact that results come from an external national catalogue and that Spanish queries work best, but the enumerated return fields are largely duplicated by the existing output schema rather than adding new behavioral context.

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?

Two tight paragraphs: purpose first, then return contents, then the usage trigger. Every sentence carries information an agent needs, with no filler or restatement of the title.

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?

Purpose, usage condition, output shape and the hand-off to read_dataset are all present, and the output schema plus annotations remove the need to dwell on return format. Minor redundancy in re-listing returned fields is the only thing keeping it from a 5.

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 coverage is 100%, so query, place and limit are all documented in the schema itself. The description reinforces the query semantics ('Spanish works best') but adds no new meaning for place or limit; baseline 3 is appropriate.

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 verb and resource — searching Spain's national open-data catalogue (datos.gob.es) for datasets on a topic — and names the sibling (read_dataset) that consumes its output. An agent can distinguish it from sibling tools like businesses or demographics without opening any schema.

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

Usage Guidelines5/5

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

Gives an explicit trigger: 'Use when a question needs data this server does not hold directly,' with concrete domains (parking, licences, local events, tourist offices, transport). It also names the follow-up tool (read_dataset) and the id needed to call it, so the routing is unambiguous.

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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