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istat_mcp_server

MCP Tool

ondata/istat_mcp_server

MCP server to query Italian statistics (ISTAT) via SDMX API — compatible with any MCP client

Install

$ npx loaditout add ondata/istat_mcp_server

Platform-specific configuration:

.claude/settings.json
{
  "mcpServers": {
    "istat_mcp_server": {
      "command": "npx",
      "args": [
        "-y",
        "istat_mcp_server"
      ]
    }
  }
}

Add the config above to .claude/settings.json under the mcpServers key.

About

ISTAT MCP Server

English | Italiano

MCP server for accessing Italian statistical data from the ISTAT SDMX API.

Overview

This Model Context Protocol (MCP) server provides Claude Desktop with access to Italian statistical data from ISTAT (Istituto Nazionale di Statistica) through the SDMX REST API. It implements a two-layer caching mechanism to minimize API calls and provides seven tools for discovering, querying, and retrieving statistical data.

Features
  • 7 MCP Tools for data discovery and retrieval:
  • discover_dataflows - Find available datasets by keywords (with blacklist filtering)
  • get_structure - Get dimension definitions and codelists for a datastructure ID
  • get_constraints - Get available constraint values for each dimension with descriptions (combines structure + constraints + codelist descriptions)
  • get_codelist_description - Get descriptions in Italian/English for codelist values
  • get_concepts - Get semantic definitions of SDMX concepts
  • get_data - Fetch actual statistical data in SDMXXML format (with blacklist validation)
  • get_cache_diagnostics - Debug tool to inspect cache status
  • Recommended Workflow (simple and efficient):
  1. Discover: Use discover_dataflows to find the dataflow you're interested in
  2. Get Complete Metadata: Use get_constraints to see all dimensions with valid values AND descriptions in one call
  • This is the RECOMMENDED approach - one call instead of many
  • Internally combines get_structure + get_codelist_description for all dimensions
  • All data cached for 1 month → subsequent calls are instant
  • Returns complete information ready for building filters in get_data
  1. Fetch Data: Use get_data with the appropriate dimension filters to retrieve actual data

Alternative workflow (manual approach):

  • Use get_structure with a datastructure ID to see dimensions and their codelists
  • Then

Tags

aiclaudedataistatitalymcpmcp-servermodel-context-protocolopen-datapythonsdmxstatistics

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Quality Signals

1
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0
Installs
Last updated23 days ago
Security: AREADME

Safety

Risk Levelmedium
Data Access
read
Network Accessnone

Details

Sourcegithub-crawl
Last commit3/22/2026
View on GitHub→

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