Kolada MCP Server

Name: Kolada MCP Server
Author: aerugo

@aerugo

代码仓库

关于 Kolada MCP Server

An MCP server for Kolada.

基本信息

分类

其他

运行时

python

传输方式

stdio

发布者

aerugo

配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "kolada-mcp": {
      "command": "python",
      "args": [
        "-m",
        "kolada_mcp"
      ]
    }
  }
}

工具

List all KPI categories with counts

Get KPIs within a specific category

Semantic search for KPIs using natural language

Get detailed metadata for a specific KPI

Fetch raw KPI data for municipalities

Comparative analysis with rankings

Compare two KPIs (difference or correlation)

List municipalities/regions

Filter by KPI threshold

概览

What is Kolada MCP Server?

A standalone MCP server that provides AI applications with access to Sweden's Kolada municipal statistics API. It enables natural language queries against thousands of Key Performance Indicators covering Swedish public sector data. Designed for developers and data analysts integrating Kolada data into AI-driven workflows.

How to use Kolada MCP Server?

Install via pip (pip install -e .) or Docker (docker-compose up -d kolada-mcp). Run in stdio mode with kolada-mcp or in HTTP mode by setting MCP_TRANSPORT=http and PORT=8001. Integrate with Claude Desktop by adding a JSON entry to its MCP server configuration.

Key features of Kolada MCP Server

9 MCP tools for comprehensive data access
Semantic search using Swedish BERT embeddings
Fully standalone (no Mima or Redis required)
Containerized with Docker support
Modern Python 3.11+ with type hints and async/await
Supports both stdio and HTTP transports

Use cases of Kolada MCP Server

List all KPI categories and their counts from Kolada
Perform natural language semantic searches for relevant KPIs
Fetch raw KPI data for specific municipalities
Compare two KPIs to analyze differences or correlations
Filter municipalities based on a KPI threshold value

FAQ from Kolada MCP Server

What MCP tools does the server provide?

It offers 9 tools: listing operating areas, getting KPIs by area, semantic KPI search, KPI metadata, raw data fetching, comparative analysis across municipalities, comparing two KPIs, listing municipalities, and filtering municipalities by KPI threshold.

What are the runtime requirements?

Python 3.11 or newer. The server has no external dependencies beyond the Kolada API and uses a standalone semantic search model.

How is the server deployed?

It can be run directly via pip (stdio mode) or as a Docker container using the provided docker-compose.yml. The transport mode is configurable between stdio and HTTP.

Where does the data come from?

All data is fetched live from Sweden's public Kolada municipal statistics API. No local data stores are used.

What configuration options are available?

Environment variables: MCP_TRANSPORT (stdio or http), PORT (HTTP server port, default 8001), and LOG_LEVEL (default INFO).

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