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Idescat MCP Server

by cat.idescat

get_idescat_stats

Obtains the complete list of Idescat statistics (all, normalized and non-normalized). It has no parameters. The response contains a 'statistics' array where each element has: 'acronym' (lowercase acronym, e.g., 'censph', to be used as the 'stat' argument of the other tools), 'name' (title), 'description' (summary), 'datasets' (boolean), 'geo' (array of available territorial disaggregations), and 'url' (initial web address, relative to https://www.idescat.cat). Use it when the user asks what statistics are available or when a statistic needs to be found by name or territorial disaggregation. THREE KEY FIELDS to decide the flow: (1) 'datasets' — if true, the statistic is normalized and you can use the rest of the tools (get_idescat_stat_tables, get_table_metadata, get_table_data, get_table_geo, render_table); if false, ONLY the information from this tool and get_idescat_stat is available. (2) 'geo' — available levels of geographical disaggregation (e.g., 'cat'=Catalonia, 'prov'=provinces, 'at'=territorial plan areas, 'com'=counties and Aran, 'mun'=municipalities, 'dis'=districts, 'sec'=census sections). ALWAYS CHECK 'geo' before choosing a statistic for a territorial query: if the user asks for county data, choose a statistic that includes 'com' in 'geo'. (3) 'url' — home page of the statistic on the Idescat website. POPULATION FIGURES: when population data is requested, there are several sources: the main and richest is CENSPH (population and housing census, annual); for semi-annual estimates there is EP (population estimates, semi-annual); and there is also PMH (municipal register of inhabitants, annual). By default, choose CENSPH (or EP if semi-annual data is requested) and NEVER use PMH for population data unless the user explicitly asks for it.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.8/5.0
Behavior5/5

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

No annotations are provided, so the description carries the full burden. It discloses the behavior in detail: no parameters, the exact structure of the returned 'statistics' array, the meaning of each key field, and what restrictions apply when 'datasets' is false. This gives the agent a clear and honest model of the tool's behavior.

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 description is well-structured and front-loaded with the primary purpose, then response fields, then usage decisions. It is longer than strictly necessary because of the detailed population-source guidance, but that guidance is genuinely useful for tool selection. The structure earns a high score; slight verbosity prevents a perfect 5.

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?

There is no output schema, so the description compensates by documenting the response array and each element's fields. It also covers normalization consequences, territorial disaggregation checks, and population-source selection. This is complete enough for an agent to invoke the tool correctly and use its results in downstream decisions.

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?

The tool has zero parameters and the schema coverage is 100%, so there are no parameter semantics for the description to add. The description correctly states that the tool has no parameters, which matches the schema. Following the baseline for parameterless tools, a 4 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?

The description begins with a specific verb and resource: 'Obtains the complete list of Idescat statistics.' It clearly defines the tool's scope (all statistics, normalized and non-normalized) and describes the response format, making it easy to distinguish from the sibling tools that operate on individual statistics or tables.

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?

The description explicitly says when to use the tool: when the user asks what statistics are available or needs to find a statistic by name or territorial disaggregation. It adds decision rules based on 'datasets' and 'geo' fields, plus specific population-source guidance (CENSPH, EP, PMH), which goes well beyond a generic usage note.

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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TDQS

A4.5/5.0
Disambiguation5/5

Each tool has a distinct role: two are statistic-level discovery tools (by acronym or full list), one lists tables, and three handle table-level metadata/data/geo rendering. The workflow progression is clear and non-overlapping.

Naming Consistency4/5

Names follow a consistent 'get_' + domain entity + optional qualifier pattern (e.g., get_idescat_stat, get_idescat_stat_tables, get_table_metadata). Slight inconsistency in that some use 'idescat' prefix and others don't, but the pattern remains predictable.

Tool Count5/5

7 tools is well-scoped for the domain: two for statistic discovery, one for table listing, and four for table-level access (metadata, data, geo, render). Each earns its place and there's no bloat.

Completeness5/5

The toolset covers the full journey from discovering statistics to drilling into tables, metadata, territorial divisions, raw JSON-stat data, and rendered Markdown. No obvious dead ends; the only minor gap is a search-by-name tool, but get_idescat_stats covers that.

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