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Thailand NSO MCP Server

by aard-ai
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Python
Remote

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.

This is a lightweight MCP that uses keyword search (this is not the Aard MCP).
It should be effective for e.g. searching for indicators or dataflows.

Note that this lightweight approach is much less effective for certain question types such as "Compare Bangkok and Chon buri", or "Tell me about trade of all fish that are not tuna". These kinds of questions benefit from structure-based search across all data sources, just two factors that make the Aard MCP different from other tools.

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 a 9-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

Tool

What it does

discover_dataflows

Search ~900 TNSO dataflows by keyword (Thai/English; any keyword matches (OR), or match_all for AND), or by covers — the dimension codes a dataflow must actually have data for (e.g. {"CWT": ["10","20"]} → every dataflow carrying both Bangkok and Chon Buri).

get_structure

Dimensions + codelists for a data structure (DSD).

get_constraints

Valid values (with labels) per dimension + available time range. Start here.

get_codelist_description

Thai/English labels for every code in a codelist.

get_concepts

Resolve an SDMX concept id to its Thai/English name.

get_data

Fetch observations as a TSV table (+ reproducible CSV/curl URLs). On an empty result it self-diagnoses (invalid codes / out-of-range period) and suggests verified non-empty alternatives.

check_data_availability

Pre-flight check that a specific filter/period combination returns rows before a full get_data.

get_territorial_codes

Thai geography codes: region (CL_AREA), province (CL_CWT, 77 changwat), district (CL_AMPHOE).

get_cache_diagnostics

Inspect the on-disk cache.

Typical workflow: discover_dataflows → get_constraints → get_data (use get_territorial_codes first when you need province/region codes). get_data self-diagnoses empty results and suggests working alternatives; use check_data_availability to pre-check a combination before fetching.

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"
discover_dataflows(covers={"CWT": ["10", "20"]})
  -> every dataflow whose data covers both Bangkok (10) and Chon Buri (20)
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).

Install Server
A
license - permissive license
A
quality
B
maintenance

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