Thailand NSO MCP Server

An MCP server that lets an LLM discover, query and analyse Thailand's official statistics from the National Statistical Office (NSO, agency TNSO) via the SDMX REST API — in natural language.

When connected to your chat tool (e.g. Gemini or Claude) it can generate analysis like the below:

This implementation based on a port of ondata/istat_mcp_server (Italy / ISTAT), adjusted to point to the Thaliand NSO SDMX endpoint at https://ns1-stathub.nso.go.th/rest.

It uses an 8-tool workflow (like the ISTAT MCP), same two-layer caching; the data source, agency, languages (Thai/English) and geography (Thai provinces) are swapped in.

Note we use Buddhist Era dates. TNSO publishes time periods in the Buddhist Era calendar (BE = Gregorian + 543). So 2567 means 2024. Pass start_period / end_period as BE years.

Tools

Typical workflow: discover_dataflows → get_constraints → get_data (use get_territorial_codes first when you need province/region codes).

Related MCP server: Ukrainian Statistics MCP Server

Install & run

Requires Python ≥ 3.11. Using uv (recommended):

git clone https://github.com/aard-ai/tnso-mcp-server.git
cd tnso-mcp-server
uv venv
uv pip install -e ".[dev]"

# Run the server (stdio transport)
uv run python -m tnso_mcp_server

Or with pip:

cd tnso-mcp-server
python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"
python -m tnso_mcp_server

Register with an MCP client

Claude Code:

claude mcp add tnso -- uv --directory /abs/path/to/tnso-mcp-server run python -m tnso_mcp_server

Claude Desktop (claude_desktop_config.json):

{
  "mcpServers": {
    "tnso": {
      "command": "uv",
      "args": ["--directory", "/abs/path/to/tnso-mcp-server", "run", "python", "-m", "tnso_mcp_server"]
    }
  }
}

Configuration

Copy .env.example to .env to override defaults (API base URL, timeouts, cache TTLs, log level, dataflow blacklist). All values have sensible defaults, so .env is optional.

Example

discover_dataflows(keywords="aging")
  -> DF_01DI_IND_AGING — "Aging Index dataflow"
get_constraints(dataflow_id="DF_01DI_IND_AGING")
  -> dimensions POP_IND, SEX, AREA, CWT, ...; TIME_PERIOD range 2557–2567 (BE)
get_territorial_codes(level="province", name="bangkok")
  -> { code: "10", name_en: "Krung Thep Maha Nakhon (Bangkok)", name_th: "กรุงเทพมหานคร" }
get_data(dataflow_id="DF_01DI_IND_AGING", dimension_filters={"CWT": ["10"]}, start_period="2560", end_period="2567")
  -> TSV table of the aging index for Bangkok, 2017–2024

Tests

uv run pytest -m "not integration"   # fast unit tests (no network)
uv run pytest -m integration         # live tests against the real TNSO API
uv run pytest                         # everything

MIT (same as the upstream istat_mcp_server).