Csu

MCP server for the Czech Statistical Office (ČSÚ / CZSO) DataStat API. Gives AI assistants direct access to 700+ statistical datasets about the Czech Republic — population, economy, prices, wages, employment, industry, agriculture, trade, tourism, environment, and more.

Single Python file. No cloning required — just uvx mcp-csu.

Features

• Full catalog access — search, browse, and inspect all 700+ datasets and 1500+ predefined tables
• Data retrieval — fetch statistical data as CSV, query individual values with full context
• AI-optimized output — human-readable text for metadata, CSV for data, automatic truncation with row counts
• Rate limiting — built-in concurrency control (3 parallel requests) and minimum request interval (150ms)
• Caching — catalog listings cached in memory for 10 minutes to avoid redundant requests
• No authentication — the DataStat API is public

Prerequisites

• uv (Python package runner)

That's it. Python and all dependencies are managed automatically by uv.

Configuration

Claude Code

Add to ~/.claude/settings.json:

{
  "mcpServers": {
    "csu": {
      "command": "uvx",
      "args": ["mcp-csu"]
    }
  }
}

Claude Desktop

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "csu": {
      "command": "uvx",
      "args": ["mcp-csu"]
    }
  }
}

Any MCP client

The server uses stdio transport (default). Launch command:

uvx mcp-csu

Data model

The DataStat database has a hierarchical structure:

Dataset (sada)              e.g. CEN0101H — "Míra inflace"
├── Dimensions (dimenze)    e.g. CasR (years), Uz0 (territory)
│   └── Items (položky)     e.g. "2024", "CZ"
├── Indicators (ukazatele)  e.g. 6134J06 — "Průměrná roční míra inflace"
└── Selections (výběry)     e.g. CEN0101HT01 — "Průměrná roční míra inflace"
    └── CSV data            pre-configured table ready to fetch

Datasets contain raw multidimensional data. Each dataset has dimensions (time, territory, categories) and indicators (what is measured).

Selections are predefined views — a specific slice of a dataset with fixed dimension arrangement. They are the easiest way to get data.

Tools

Discovery

search_datasets

Full-text search across all datasets. Returns dataset codes, names, time period types, and territory levels.

Parameter	Type	Required	Description
query	string	yes	Search keyword (Czech recommended)

search_datasets("inflace")
→ Found 3 dataset(s):
    WCEN01 (v4) — Index spotřebitelských cen (indexy, míra inflace)
    WCEN01M (v1) — Index spotřebitelských cen — měsíční data
    CEN0101H (v1) — Míra inflace

search_selections

Full-text search across all predefined data tables.

Parameter	Type	Required	Description
query	string	yes	Search keyword (Czech recommended)

search_selections("mzdy")
→ Found 30 selection(s):
    MZDQ1T1 — Průměrný evidenční počet zaměstnanců a průměrné hrubé měsíční mzdy...
      Period: Čtvrtletí | Territory: Stát | Dataset: MZDQ1

list_datasets

Paginated listing of all datasets.

Parameter	Type	Default	Description
offset	int	0	Skip first N items
limit	int	30	Items per page (max 100)

list_selections

Paginated listing of all predefined tables.

Parameter	Type	Default	Description
offset	int	0	Skip first N items
limit	int	30	Items per page (max 100)

Exploration

get_dataset

Full dataset metadata: dimensions with item counts, indicators with definitions, keywords, update frequency.

Parameter	Type	Required	Description
dataset_code	string	yes	Dataset code (e.g. CEN0101H)

get_dataset("CEN0101H")
→ Dataset: CEN0101H (v1)
  Name: Míra inflace
  Keywords: míra inflace
  Update frequency: MONTHLY

  Dimensions (4):
    CasM — Měsíce (720 items)
    CasR — Roky (61 items)
    CASRMX — Měsíce, roky (780 items)
    Uz0 — Území (1 items)

  Indicators (4):
    6134J09 — Přírůstek průměrného ročního indexu spotřebitelských cen - měsíční
    6134J06 — Průměrná roční míra inflace
    ...

get_dataset_selections

List predefined data tables for a specific dataset.

Parameter	Type	Required	Description
dataset_code	string	yes	Dataset code

get_dataset_selections("CEN0101H")
→ Selections for CEN0101H (2):
    CEN0101HT01 — Průměrná roční míra inflace
      Period: Rok | Territory: Stát
    CEN0101HT02 — Míra inflace - měsíční
      Period: Měsíc | Territory: Stát

get_dimension_items

Get all possible values for a dimension. Supports hierarchy level filtering and pagination.

Parameter	Type	Default	Description
dimension_code	string	—	Dimension code from get_dataset()
level	string	null	Filter by hierarchy level (e.g. STAT, KRAJ, OKRES)
offset	int	0	Skip first N items
limit	int	50	Items per page (max 200)

get_dimension_items("UZ023H2U", level="KRAJ")
→ Dimension UZ023H2U — 14 item(s) at level KRAJ:
    CZ010 — Hlavní město Praha (Capital City Prague) [KRAJ]
    CZ020 — Středočeský kraj (Central Bohemian Region) [KRAJ]
    CZ031 — Jihočeský kraj (South Bohemian Region) [KRAJ]
    ...

get_indicator

Indicator definition and display format.

Parameter	Type	Required	Description
indicator_code	string	yes	Indicator code from get_dataset()

Data retrieval

get_selection_data

Primary data access tool. Fetches CSV data from a predefined selection.

Parameter	Type	Default	Description
selection_code	string	—	Selection code (e.g. CEN0101HT01)
max_rows	int	100	Max data rows. 0 = unlimited

get_selection_data("CEN0101HT01", max_rows=5)
→ "Ukazatel","Území","Roky","Hodnota"
  "Průměrná roční míra inflace","Česko","2025","2.5"
  "Průměrná roční míra inflace","Česko","2024","2.4"
  "Průměrná roční míra inflace","Česko","2023","10.7"
  "Průměrná roční míra inflace","Česko","2022","15.1"
  "Průměrná roční míra inflace","Česko","2021","3.8"

  [Showing 5 of 29 rows. Use max_rows=0 for all data or max_rows=10 to see more.]

get_value

Retrieve a single specific value. The most precise query — returns one number with full context (indicator name, dimension labels, publication date).

Parameter	Type	Default	Description
dataset_code	string	—	Dataset code
indicator_code	string	—	Indicator code
dimension_codes	list[str]	—	Dimension codes in order
item_codes	list[str]	—	Item codes matching dimensions
version	string	null	Dataset version (latest if omitted)

get_value("RSO01", "3971b",
          ["CasR", "TYPPROSJED", "UZ023H2U"],
          ["2023", "501", "CZ"])
→ Value: 6 258
  Indicator: Počet územních jednotek
    Roky: 2023
    Typ prostorové jednotky: Obec
    ČR, kraje, okresy: Česko
  Published: 2024-04-30T07:00:00Z

custom_query

Execute an arbitrary data query on a dataset. Returns CSV.

This is an advanced tool — prefer get_selection_data() when a suitable predefined table exists. The custom query API is sensitive to correct dimension placement and hierarchy level filtering.

Parameter	Type	Default	Description
dataset_code	string	—	Dataset code
dataset_version	string	—	Version from get_dataset()
columns	list[dict]	—	Column dimensions (each needs kodDimenze)
rows	list[dict]	—	Row dimensions
table_filters	list[dict]	null	Filter dimensions
max_rows	int	100	Max CSV rows

get_dataset_metadata

Dataset content statistics: record count, time range, publication and update timestamps.

Parameter	Type	Required	Description
dataset_code	string	yes	Dataset code
version	string	yes	Version from get_dataset()

Usage examples

Get Czech inflation rate

1. search_datasets("inflace")
   → CEN0101H — Míra inflace

2. get_dataset_selections("CEN0101H")
   → CEN0101HT01 — Průměrná roční míra inflace

3. get_selection_data("CEN0101HT01")
   → CSV with annual inflation rates from 1994 to present

Find average wages by region

1. search_selections("mzdy kraje")
   → MZDQ1T2 — ... dle krajů a regionů soudržnosti

2. get_selection_data("MZDQ1T2", max_rows=20)
   → CSV with wages by region

Get exact population of Prague in 2023

1. search_datasets("obyvatelstvo")
   → OBY01 — Obyvatelstvo podle pohlaví a věku

2. get_dataset("OBY01")
   → see dimensions and indicators

3. get_dimension_items("<territory_dim>", level="KRAJ")
   → find Prague code

4. get_value("OBY01", "<indicator>",
             ["<time_dim>", "<territory_dim>"],
             ["2023", "<prague_code>"])
   → exact value

Czech vocabulary for search

The database is in Czech. Common search terms:

Czech	English	Example datasets
obyvatelstvo	population	OBY01, OBY02
mzdy	wages	MZDQ1, MZD01
ceny	prices	CEN01, CEN02
inflace	inflation	CEN0101H
HDP	GDP	NUC06R, NUC06Q
nezaměstnanost	unemployment	ZAM04
průmysl	industry	PRU01
stavebnictví	construction	STA01
vzdělání	education	VZD01
zdraví	health	ZDR01
zemědělství	agriculture	ZEM01
doprava	transport	DOP01
cestovní ruch	tourism	CRU01
životní prostředí	environment	ZPR01
kriminalita	crime	KRI01
volby	elections	VOL01
bytová výstavba	housing	BYT01
zahraniční obchod	foreign trade	VZO01

Technical details

Architecture

Single-file Python server using FastMCP framework over stdio transport. Dependencies managed via PEP 723 inline script metadata — uv run installs them automatically into an isolated environment.

Upstream API

The server wraps two DataStat REST APIs:

API	Base URL	Purpose
Catalog	https://data.csu.gov.cz/api/katalog/v1	Dataset/selection/dimension/indicator metadata
Data	https://data.csu.gov.cz/api/dotaz/v1	Data retrieval (CSV, JSON-STAT)

API documentation:

• General info
• Catalog Swagger
• Data Swagger

Rate limiting

The DataStat API does not document rate limits, but the server applies conservative throttling:

• Max concurrent requests: 3 (semaphore)
• Min request interval: 150ms (global)
• Request timeout: 60 seconds

Caching

Catalog listings (list_datasets, list_selections) are cached in memory with a 10-minute TTL. These endpoints return the full catalog (700–1500 items) on every call since the API ignores pagination parameters — caching avoids repeated large transfers.

Output formatting

• Metadata tools return structured text with clear labels
• Data tools return CSV (most compact and LLM-friendly tabular format)
• Truncation: data responses are limited to 100 rows by default, with total count shown. Adjustable via max_rows parameter
• Language: all API responses are in Czech (Accept-Language: cs)

Dependencies

Package	Version	Purpose
mcp	>=1.0.0	MCP server framework (FastMCP)
httpx	>=0.27.0	Async HTTP client

Both installed automatically by uv run.

License

MIT