Opendatasoft MCP Server

Opendatasoft MCP Server

By Mark-Friese GitHub

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opendatasoft mcp-server
Overview

What is Opendatasoft MCP Server?

The Opendatasoft MCP Server is a Model Context Protocol (MCP) server that facilitates interaction with the Opendatasoft Explore API v2.1, enabling AI assistants to search, query, and analyze open datasets.

How to use Opendatasoft MCP Server?

To use the Opendatasoft MCP Server, install it from source, configure it with your environment variables, and integrate it with an MCP-compatible client like Claude for Desktop.

Key features of Opendatasoft MCP Server?

  • Dataset Discovery: Search and browse datasets by keywords, publishers, and themes.
  • Dataset Exploration: View schemas, metadata, and sample records.
  • Data Querying: Execute ODSQL queries with filtering, sorting, and aggregation.
  • Data Analysis: Generate statistics, analyze fields, and visualize distributions.
  • Data Export: Generate export URLs for various formats (CSV, JSON, GeoJSON, etc.).

Use cases of Opendatasoft MCP Server?

  1. Searching for datasets related to specific topics.
  2. Analyzing data distributions and statistics.
  3. Exporting datasets in various formats for further analysis.

FAQ from Opendatasoft MCP Server?

  • What is the ODSQL?

ODSQL is the Opendatasoft Query Language used for filtering, aggregating, and sorting data.

  • What are the requirements to run the server?

You need Python 3.10 or later and the Model Context Protocol (MCP) SDK 1.2.0 or later.

  • Is there a license for this project?

Yes, the project is licensed under the MIT License.

Content

Opendatasoft MCP Server

A Model Context Protocol (MCP) server that provides tools for interacting with the Opendatasoft Explore API v2.1, enabling AI assistants like Claude to search, query, and analyze open datasets.

Features

  • Dataset Discovery: Search and browse datasets by keywords, publishers, and themes
  • Dataset Exploration: View schemas, metadata, and sample records
  • Data Querying: Execute ODSQL queries with filtering, sorting, and aggregation
  • Data Analysis: Generate statistics, analyze fields, and visualize distributions
  • Data Export: Generate export URLs for various formats (CSV, JSON, GeoJSON, etc.)

Installation

Requirements

Installing from Source

  1. Clone the repository:

    git clone https://github.com/your-username/opendatasoft-mcp-server.git
    cd opendatasoft-mcp-server
    
  2. Create a virtual environment and install dependencies:

    python -m venv venv
    source venv/bin/activate  # On Windows: venv\Scripts\activate
    pip install -e .
    

Configuration

The server can be configured using environment variables:

Usage with Claude for Desktop

  1. Make sure you have Claude for Desktop installed. You can download it from claude.ai/download.

  2. Configure Claude for Desktop to use this MCP server by adding it to your Claude for Desktop configuration file:

    • On macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
    • On Windows: %APPDATA%\Claude\claude_desktop_config.json
    {
      "mcpServers": {
        "opendatasoft": {
          "command": "/path/to/venv/bin/python",
          "args": ["-m", "src.main"],
          "env": {
            "ODS_BASE_URL": "https://documentation-resources.opendatasoft.com",
            "ODS_API_KEY": "your-api-key-if-needed"
          }
        }
      }
    }
    
  3. Restart Claude for Desktop.

  4. You can now use the Opendatasoft MCP server in your conversations with Claude.

Available Tools

Catalog Tools

  • search_datasets: Search for datasets by keyword
  • get_dataset_info: Get detailed information about a specific dataset
  • list_datasets_by_publisher: List datasets from a specific publisher
  • list_dataset_fields: List all fields in a dataset with their types and descriptions

Query Tools

  • get_dataset_records: Get records from a dataset with optional filtering and sorting
  • get_dataset_aggregates: Get aggregated data from a dataset using ODSQL aggregation functions
  • facet_analysis: Analyze facet values distribution for a dataset
  • search_dataset_records: Search for specific records within a dataset
  • get_export_url: Get a URL for exporting dataset records in various formats

Analysis Tools

  • summarize_dataset: Generate a comprehensive summary of a dataset
  • analyze_numeric_field: Analyze a numeric field, including min, max, average, and distribution
  • analyze_text_field: Analyze a text field, including value frequency
  • analyze_date_field: Analyze a date field, including range, distribution by year/month
  • generate_dataset_statistics: Generate comprehensive statistics for all fields in a dataset

Example Queries for Claude

Here are some example queries you can ask Claude while using this MCP server:

  • "Find datasets related to transportation."
  • "Show me datasets published by the World Food Programme."
  • "What are the fields in the 'world-administrative-boundaries' dataset?"
  • "Get 5 records from the 'gold-prices' dataset."
  • "Count the number of cities per country in the 'geonames-all-cities-with-a-population-1000' dataset."
  • "Analyze the 'population' field in the 'world-administrative-boundaries' dataset."
  • "What's the distribution of records by year in the 'gold-prices' dataset?"
  • "Generate a CSV export URL for the 'gold-prices' dataset with prices sorted by date."

Understanding ODSQL

Many of the tools in this MCP server use the Opendatasoft Query Language (ODSQL) for filtering, aggregating, and sorting data. Here are some basic examples:

Select Clause

Choosing which fields to return:

select=field1, field2, field3

Aggregation:

select=count(*) as total, avg(field) as average

Where Clause

Filtering records:

where=field > 100
where=date_field >= date'2020-01-01'
where=text_field like "Paris"

Full-text search:

where=search(field, "keyword")

Group By Clause

Grouping results:

group_by=field
group_by=year(date_field)

Order By Clause

Sorting results:

order_by=field ASC
order_by=field DESC

For more details on ODSQL syntax, see the Opendatasoft documentation.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

No tools information available.

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