About Kolada MCP Server
Kolada MCP Server gives AI applications access to Sweden’s Kolada municipal and regional statistics API. It offers KPI search, metadata and data retrieval, plus comparison and filtering tools for public-sector analysis.
Key features
- 01Nine tools for Kolada data
- 02Semantic KPI search
- 03KPI metadata and data retrieval
- 04Municipality and region comparisons
- 05Filter municipalities by KPI thresholds
Use cases
- 01Finding KPIs with natural-language queries
- 02Comparing municipal KPI results
- 03Filtering municipalities by indicator thresholds
MCP support
The MCP server exposes nine tools for searching Kolada indicators, retrieving data, and analyzing municipalities and regions. It supports stdio and HTTP transport modes.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEKolada MCP Server
A clean, standalone MCP (Model Context Protocol) server for Sweden's Kolada municipal statistics API.
Overview
This server provides AI applications with access to Sweden's comprehensive municipal and regional statistics database. It enables natural language queries against thousands of Key Performance Indicators (KPIs) covering various aspects of Swedish public sector data.
Features
- 9 MCP Tools for comprehensive data access
- Semantic Search using Swedish BERT embeddings
- No External Dependencies - fully standalone (no Mima or Redis)
- Containerized with Docker support
- Modern Python 3.11+ with type hints and async/await
Available Tools
| Tool | Description |
|---|---|
list_operating_areas | List all KPI categories with counts |
get_kpis_by_operating_area | Get KPIs within a specific category |
search_kpis | Semantic search for KPIs using natural language |
get_kpi_metadata | Get detailed metadata for a specific KPI |
fetch_kolada_data | Fetch raw KPI data for municipalities |
analyze_kpi_across_municipalities | Comparative analysis with rankings |
compare_kpis | Compare two KPIs (difference or correlation) |
list_municipalities | List municipalities/regions |
filter_municipalities_by_kpi | Filter by KPI threshold |
Installation
Using pip
pip install -e .
Using Docker
docker-compose up -d kolada-mcp
Usage
Stdio Mode (Default)
kolada-mcp
# or
python -m kolada_mcp
HTTP Mode
MCP_TRANSPORT=http PORT=8001 kolada-mcp
Claude Desktop Integration
Add to your Claude Desktop configuration:
{
"mcpServers": {
"kolada": {
"command": "kolada-mcp"
}
}
}
Configuration
Environment variables:
| Variable | Default | Description |
|---|---|---|
MCP_TRANSPORT | stdio | Transport mode (stdio or http) |
PORT | 8001 | HTTP server port |
LOG_LEVEL | INFO | Logging level |
Development
Setup
# Create virtual environment
python -m venv .venv
source .venv/bin/activate
# Install with dev dependencies
pip install -e ".[dev]"
Running Tests
pytest
Code Quality
# Linting
ruff check src tests
# Type checking
mypy src
Architecture
src/kolada_mcp/
├── __init__.py # Package init
├── __main__.py # Entry point
├── config.py # Settings (Pydantic)
├── server.py # FastMCP server
├── models/
│ └── types.py # Data models
├── services/
│ ├── kolada_client.py # API client
│ ├── embeddings.py # Semantic search
│ └── data_processing.py
└── tools/
├── metadata.py # KPI metadata tools
├── data.py # Data fetching tools
├── municipality.py # Municipality tools
└── comparison.py # Comparison tools
License
Apache-2.0
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.