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JSON (mcpServers)Claude CodeCodexOpenCodeCursorVS CodeGemini CLIGoose

{
  "mcpServers": {
    "idescat": {
      "type": "http",
      "url": "https://api.idescat.cat/mcp"
    }
  }
}

claude mcp add --transport http idescat https://api.idescat.cat/mcp

[mcp_servers.idescat]
url = "https://api.idescat.cat/mcp"

{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "idescat": {
      "type": "remote",
      "url": "https://api.idescat.cat/mcp",
      "enabled": true
    }
  }
}

{
  "mcpServers": {
    "idescat": {
      "type": "http",
      "url": "https://api.idescat.cat/mcp"
    }
  },
  "deeplink": "cursor://anysphere.cursor-deeplink/mcp/install?name=idescat&config=eyJ0eXBlIjoiaHR0cCIsInVybCI6Imh0dHBzOi8vYXBpLmlkZXNjYXQuY2F0L21jcCJ9"
}

{
  "servers": {
    "idescat": {
      "type": "http",
      "url": "https://api.idescat.cat/mcp"
    }
  }
}

{
  "mcpServers": {
    "idescat": {
      "httpUrl": "https://api.idescat.cat/mcp"
    }
  }
}

extensions:
  "idescat":
    enabled: true
    name: "idescat"
    type: streamable_http
    uri: "https://api.idescat.cat/mcp"
    timeout: 300

Quality 85 / 100good

protocol100 · weight 25

pass
protocol.modernsupports 2026-07-28 (server/discover)
pass
protocol.statelessanswers without a session
pass
protocol.transportstreamable HTTP
pass
protocol.list_ttltool list is cacheable (ttlMs 86400000)

authorizationn/a · weight 15

n/a
auth.prm, auth.as_metadata, auth.cimdno remote uses OAuth
n/a
auth.secret_in_urlno templated URL

tool hygiene64 · weight 30

pass
tools.descriptionsevery tool has a description
warn
tools.description_length5 tools have descriptions over 1,024 characters, which crowds the context
pass
tools.schemasevery tool has an object input schema
fail
tools.annotationsno tool declares readOnlyHint or destructiveHint, so clients cannot tell safe calls from risky ones
fail
tools.directory_hintsno tool declares all four of readOnlyHint, destructiveHint, idempotentHint and openWorldHint; missing: get_table_data (readOnlyHint, destructiveHint, idempotentHint, openWorldHint); get_table_geo (readOnlyHint, destructiveHint, idempotentHint, openWorldHint); get_idescat_stats (readOnlyHint, destructiveHint, idempotentHint, openWorldHint); …
pass
tools.token_costabout 4,316 tokens to load every tool
pass
tools.api_dumptools are not a one-to-one API dump

stability100 · weight 15

pass
stability.changes2 tool changes in 30 days
pass
stability.rug_pullno tool changed its meaning under the same name

dependenciesn/a · weight 15

n/a
deps.known_vulns, deps.mcp_sdk_version, deps.resolvableno npm or PyPI package

7 tools, about 4,316 tokens to load them all · tool set 3a9bfc7d0eac · tools last changed 2026-10-14 · checked 2026-10-19 21:42 UTC

Tool changes

2026-10-14render_tablechangeddescription, input schema
before
Returns a Markdown table already built with the filtered data from an Idescat table, ready to be shown to the user without having to interpret the JSON-stat. It includes the title, units, the table with category labels, status notes (e.g., provisional data), and ALWAYS a final link to the table on the Idescat website that must be kept in the response to the user. By default, it puts the time dimension in rows and the first dimension with more than one category in columns; this can be changed with the 'rows' and 'cols' parameters. All other dimensions with more than one category must be reduced to a single category with 'filters'. IMPORTANT (error self-correction): if the tool returns the error with 'pending_dimensions', DO NOT change tools: call render_table again adding the 'filters' parameter with a single category for each pending dimension (you can start from the 'suggested_filters' field that comes in the same error response, but choose the appropriate category for the user's question according to 'categories'). If it then returns the size error (maximum 40 rows × 15 columns), follow the 'how_to_fix' field: if the time dimension has too many columns, add the 'last' parameter (e.g., last: 12), and if the rows dimension is too large, filter it with 'filters' or swap 'rows'/'cols'. You can chain 'filters' and 'last' in the same call. The values '..' indicate data not available.
after · 86% of words in common
Returns a Markdown table already built with the filtered data from an Idescat table, ready to be shown to the user without having to interpret the JSON-stat. It includes the title, units, the table with category labels, status notes (e.g., provisional data), and ALWAYS a final link to the table on the Idescat website that must be kept in the response to the user. By default, it puts the time dimension in rows and the first dimension with more than one category in columns; this can be changed with the 'rows' and 'cols' parameters. All other dimensions with more than one category must be reduced to a single category with 'filters'. IMPORTANT (error self-correction): if the tool returns the error with 'pending_dimensions', DO NOT change tools: call render_table again adding the 'filters' parameter with a single category for each pending dimension (you can start from the 'suggested_filters' field that comes in the same error response, but choose the appropriate category for the user's question according to 'categories'). If it then returns the size error (maximum 40 rows × 15 columns), follow the 'how_to_fix' field: if the time dimension has too many columns, add the 'last' parameter (e.g., last: 12), and if the rows dimension is too large, filter it with 'filters' or swap 'rows'/'cols'. You can chain 'filters' and 'last' in the same call. The values '..' indicate data not available. CALCULATIONS: never add, subtract or compute percentages yourself from the returned values; use filters ranges, sum and derive and report the server's result, checking that the Calculations notes under the table (codes used, warnings) match the question.
2026-10-14get_table_datachangeddescription, input schema
before
Retrieves the data (values) of an Idescat table in JSON-stat format for a territorial division. You can retrieve the ENTIRE table (without filters) or only a SUBSET by passing the OPTIONAL parameters 'filters' (specific categories per dimension) and/or 'last' (number of last time periods); filtering returns smaller responses and allows querying tables that as a whole would exceed the limit. The filters use the dimension identifiers and category codes (category.index) returned by get_table_metadata (with the same 'geo'). IMPORTANT: the 20,000 cell limit is checked AFTER applying the filters; if it exceeds it, it returns a 'Dataset too large' error and you must add more filters (filters, last). You can call get_table_metadata beforehand to know the dimensions, periods, and category codes. The result contains: 'id' (dimension names in order), 'size' (number of categories per dimension), 'value' (flat array of values), 'dimension' (details per dimension: codes in category.index, labels in category.label, units in category.unit), 'role' (which dimensions are time, geo, or metric), 'status' (marks special values). HOW TO READ 'value' (row-major order): the array is FLAT; the LAST dimension of 'id' varies fastest and the first slowest. To get the value of a combination of categories (one per dimension) follow these 4 steps: (1) for each dimension, look for the POSITION (0-based) of the chosen category in dimension.{nom}.category.index — this 'index' can be a code→position object or a list of codes (the position is the index within the list); ALWAYS use this index, DO NOT assume alphabetical order or by label; (2) the stride of each dimension = product of the 'size' of the SUBSEQUENT dimensions (the last dimension has stride 1); (3) offset = sum of (position × stride) of all dimensions; (4) the searched value is value[offset]. Use the order of the 'id' array in all steps (NOT the order of keys in 'dimension'). EXAMPLE: id=['territory','sex'], size=[3,2], territory.index={BCN:0,GIR:1,LLE:2}, sex.index={M:0,F:1} → strides=[2,1]; for Girona+Woman: offset = 1×2 + 1×1 = 3, i.e., value[3]. Canonical ordering sample: https://json-stat.org/samples/order.json. WARNINGS: 1) null values (data not available) OCCUPY their position within the order: they DO NOT shift the index. 2) 'Base 2024', 'Base 2024=100' or '2024 Statistical Revision' in the title DO NOT indicate estimates; they are only the year of methodological reference and the data are real and definitive unless 'status' indicates otherwise.
after · 91% of words in common
Retrieves the data (values) of an Idescat table in JSON-stat format for a territorial division. You can retrieve the ENTIRE table (without filters) or only a SUBSET by passing the OPTIONAL parameters 'filters' (specific categories per dimension) and/or 'last' (number of last time periods); filtering returns smaller responses and allows querying tables that as a whole would exceed the limit. The filters use the dimension identifiers and category codes (category.index) returned by get_table_metadata (with the same 'geo'). IMPORTANT: the 20,000 cell limit is checked AFTER applying the filters; if it exceeds it, it returns a 'Dataset too large' error and you must add more filters (filters, last). You can call get_table_metadata beforehand to know the dimensions, periods, and category codes. The result contains: 'id' (dimension names in order), 'size' (number of categories per dimension), 'value' (flat array of values), 'dimension' (details per dimension: codes in category.index, labels in category.label, units in category.unit), 'role' (which dimensions are time, geo, or metric), 'status' (marks special values). HOW TO READ 'value' (row-major order): the array is FLAT; the LAST dimension of 'id' varies fastest and the first slowest. To get the value of a combination of categories (one per dimension) follow these 4 steps: (1) for each dimension, look for the POSITION (0-based) of the chosen category in dimension.{nom}.category.index — this 'index' can be a code→position object or a list of codes (the position is the index within the list); ALWAYS use this index, DO NOT assume alphabetical order or by label; (2) the stride of each dimension = product of the 'size' of the SUBSEQUENT dimensions (the last dimension has stride 1); (3) offset = sum of (position × stride) of all dimensions; (4) the searched value is value[offset]. Use the order of the 'id' array in all steps (NOT the order of keys in 'dimension'). EXAMPLE: id=['territory','sex'], size=[3,2], territory.index={BCN:0,GIR:1,LLE:2}, sex.index={M:0,F:1} → strides=[2,1]; for Girona+Woman: offset = 1×2 + 1×1 = 3, i.e., value[3]. Canonical ordering sample: https://json-stat.org/samples/order.json. WARNINGS: 1) null values (data not available) OCCUPY their position within the order: they DO NOT shift the index. 2) 'Base 2024', 'Base 2024=100' or '2024 Statistical Revision' in the title DO NOT indicate estimates; they are only the year of methodological reference and the data are real and definitive unless 'status' indicates otherwise. CALCULATIONS: never add, subtract or compute percentages yourself from the returned values; use filters ranges, sum and derive and report the server's result, checking that extension.derived (codes used, warnings) matches the question.

Full history: list_changes · trend: quality JSON.

Checks run on what our credential-free, read-only probes observe; tools are never called and no code audit is performed. How it is computed.

Trust 100 / 100

provenance100

liveness100

freshness100

hygiene100

+repository +namespace-matches-repo +dns-namespace +website +reachable +uptime-100% +updated-3d-ago

Derived from observable signals (official registry feed and our own credential-free probes); no code audit performed. How it is computed.

Endpoints

remote	auth	reachable	uptime 30d	p50	protocol	last ok
`https://api.idescat.cat/mcp` streamable-http	none	yes	100%	558 ms	2026-07-28	2026-10-19

Tools (7)

`get_idescat_stat`	Obtains information about an Idescat (Statistical Institute of Catalonia) statistic given its acronym. Use it when the user asks about a specific statistic identified by its acronym (for example, CENSPH, EPA, PIBC, AFI, etc.). The 'datasets' field is a key boolean: if true, the statistic is normalized and therefore available in get_idescat_stat_tables and the rest of the table-level tools (metadata, data, divisions, etc.); if false, the statistic is NOT normalized and get_idescat_stat_tables will not recognize it (it would return an identifier error), so the only information available is what this same tool returns ('url', 'time_ref', 'update_date', 'update_title', 'update_text'). To know if a statistic has queryable tables, this tool is enough, without calling get_idescat_stat_tables. The 'url' field is a relative address to https://www.idescat.cat. The 'geo' field is an array with the available geographical disaggregation levels (e.g., 'cat', 'prov', 'at', 'com', 'mun', 'dis', 'sec'). POPULATION FIGURES: when population data is requested, the main and richest source 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.
`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.
`get_idescat_stat_tables`	Obtains the list of tables for an Idescat statistic given its official acronym. Use it when the user asks what tables or information a statistic has. The response contains a 'tables' array where each element has 'id' (numerical identifier) and 'name' (table title). Present each table as: name (id). Inform the user that they can request more details about a table by indicating its identifier.
`get_table_data`	Retrieves the data (values) of an Idescat table in JSON-stat format for a territorial division. You can retrieve the ENTIRE table (without filters) or only a SUBSET by passing the OPTIONAL parameters 'filters' (specific categories per dimension) and/or 'last' (number of last time periods); filtering returns smaller responses and allows querying tables that as a whole would exceed the limit. The filters use the dimension identifiers and category codes (category.index) returned by get_table_metadata (with the same 'geo'). IMPORTANT: the 20,000 cell limit is checked AFTER applying the filters; if it exceeds it, it returns a 'Dataset too large' error and you must add more filters (filters, last). You can call get_table_metadata beforehand to know the dimensions, periods, and category codes. The result contains: 'id' (dimension names in order), 'size' (number of categories per dimension), 'value' (flat array of values), 'dimension' (details per dimension: codes in category.index, labels in category.label, units in category.unit), 'role' (which dimensions are time, geo, or metric), 'status' (marks special values). HOW TO READ 'value' (row-major order): the array is FLAT; the LAST dimension of 'id' varies fastest and the first slowest. To get the value of a combination of categories (one per dimension) follow these 4 steps: (1) for each dimension, look for the POSITION (0-based) of the chosen category in dimension.{nom}.category.index — this 'index' can be a code→position object or a list of codes (the position is the index within the list); ALWAYS use this index, DO NOT assume alphabetical order or by label; (2) the stride of each dimension = product of the 'size' of the SUBSEQUENT dimensions (the last dimension has stride 1); (3) offset = sum of (position × stride) of all dimensions; (4) the searched value is value[offset]. Use the order of the 'id' array in all steps (NOT the order of keys in 'dimension'). EXAMPLE: id=['territory','sex'], size=[3,2], territory.index={BCN:0,GIR:1,LLE:2}, sex.index={M:0,F:1} → strides=[2,1]; for Girona+Woman: offset = 1×2 + 1×1 = 3, i.e., value[3]. Canonical ordering sample: https://json-stat.org/samples/order.json. WARNINGS: 1) null values (data not available) OCCUPY their position within the order: they DO NOT shift the index. 2) 'Base 2024', 'Base 2024=100' or '2024 Statistical Revision' in the title DO NOT indicate estimates; they are only the year of methodological reference and the data are real and definitive unless 'status' indicates otherwise. CALCULATIONS: never add, subtract or compute percentages yourself from the returned values; use filters ranges, sum and derive and report the server's result, checking that extension.derived (codes used, warnings) matches the question.
`get_table_geo`	Obtains the list of territorial divisions available for an Idescat table (for example: cat=Catalonia, com=counties and Aran, mun=municipalities, prov=provinces, at=territorial plan areas). The response contains a 'divisions' array where each element has 'id' (division code) and 'name' (label). Often it is NOT necessary to call it because get_table_metadata already returns these divisions in 'link.related'. Use it only if you need the list of divisions without having called the metadata. The 'id' code is the value to pass to the 'geo' parameter of get_table_metadata, get_table_data, or render_table. If not specified, these tools use the 'cat' division (Catalonia as a whole) by default.
`get_table_metadata`	Obtains the metadata for a specific Idescat table in JSON-stat format, given the statistic acronym (stat) and the table identifier (table). Use it when the user wants to know the details of a table: dimensions, categories available for each dimension (time periods, territorial scopes, sex, etc.), date of last update, units, etc. The acronym and identifier are previously obtained with get_idescat_stat_tables. At the end of the response, always include this Markdown link (substituting {stat} and {table} in lowercase): [See the results in this table](https://www.idescat.cat/pub/?id={stat}&n={table}). TERRITORIAL DIVISIONS: the response includes 'link.related', an array with other available territorial divisions (provinces, counties, municipalities, etc.); therefore it is often NOT necessary to call get_table_geo. If you pass the 'geo' parameter (for example geo='com' or geo='mun'), you get the metadata for that division, which includes the codes and labels (category.index / category.label) for the geographical dimension and the rest of the dimensions; use these codes directly to build the 'filters' parameter of get_table_data or render_table, without having to test and correct. SIZE CONTROL: check the 'size' field (array): the product of all its elements is the total number of cells. If it exceeds 20,000 (common case for municipal data), DO NOT retrieve the entire table; use get_table_data with filters (filters, last) to reduce the selection. If it is equal to or less than 20,000, you can retrieve it entirely with get_table_data.
`render_table`	Returns a Markdown table already built with the filtered data from an Idescat table, ready to be shown to the user without having to interpret the JSON-stat. It includes the title, units, the table with category labels, status notes (e.g., provisional data), and ALWAYS a final link to the table on the Idescat website that must be kept in the response to the user. By default, it puts the time dimension in rows and the first dimension with more than one category in columns; this can be changed with the 'rows' and 'cols' parameters. All other dimensions with more than one category must be reduced to a single category with 'filters'. IMPORTANT (error self-correction): if the tool returns the error with 'pending_dimensions', DO NOT change tools: call render_table again adding the 'filters' parameter with a single category for each pending dimension (you can start from the 'suggested_filters' field that comes in the same error response, but choose the appropriate category for the user's question according to 'categories'). If it then returns the size error (maximum 40 rows × 15 columns), follow the 'how_to_fix' field: if the time dimension has too many columns, add the 'last' parameter (e.g., last: 12), and if the rows dimension is too large, filter it with 'filters' or swap 'rows'/'cols'. You can chain 'filters' and 'last' in the same call. The values '..' indicate data not available. CALCULATIONS: never add, subtract or compute percentages yourself from the returned values; use filters ranges, sum and derive and report the server's result, checking that the Calculations notes under the table (codes used, warnings) match the question.

Schemas: list_tools.

Similar MCP servers

Official server.json

{
  "name": "cat.idescat/mcp",
  "title": "Idescat MCP Server",
  "$schema": "https://static.modelcontextprotocol.io/schemas/2025-12-11/server.schema.json",
  "remotes": [
    {
      "url": "https://api.idescat.cat/mcp",
      "type": "streamable-http"
    }
  ],
  "version": "1.1.0",
  "repository": {
    "url": "https://github.com/idescat/mcp",
    "source": "github"
  },
  "websiteUrl": "https://www.idescat.cat/",
  "description": "Query official statistics of Catalonia (Idescat): tables, metadata and JSON-stat data via MCP."
}