🔭 EOSC Data Commons Search server

A server for the EOSC Data Commons project MatchMaker service, providing natural language search over open-access datasets. It exposes an HTTP POST endpoint and supports the Model Context Protocol (MCP) to help users discover datasets and tools via a Large Language Model–assisted search.

🧩 Endpoints

The HTTP API comprises 2 main endpoints:

/mcp: MCP server that searches for relevant data to answer a user question using the EOSC Data Commons OpenSearch service
- Uses Streamable HTTP transport
- Available tools:
  - Search datasets
  - Get metadata for the files in a dataset (name, description, type of files)
  - Search tools
  - Search citations related to datasets or tools
/chat: HTTP POST endpoint (JSON) for chatting with the MCP server tools via an LLM provider (API key provided through env variable at deployment)
- Streams Server-Sent Events (SSE) response complying with the AG-UI protocol.

[!TIP]

It can also be used just as a MCP server through the pip package.

🔌 Connect client to MCP server

The system can be used directly as a MCP server using either STDIO, or Streamable HTTP transport.

[!WARNING]

You will need access to a pre-indexed OpenSearch instance for the MCP server to work.

Follow the instructions of your client, and use the /mcp URL of your deployed server (e.g. http://localhost:8000/mcp)

To add a new MCP server to VSCode GitHub Copilot:

Open the Command Palette (ctrl+shift+p or cmd+shift+p)
Search for MCP: Add Server...
Choose HTTP, and provide the MCP server URL http://localhost:8000/mcp

Your VSCode mcp.json should look like:

{
    "servers": {
        "data-commons-search-http": {
            "url": "http://localhost:8000/mcp",
            "type": "http"
        }
    },
    "inputs": []
}

Or with STDIO transport:

{
   "servers": {
      "data-commons-search": {
         "type": "stdio",
         "command": "uvx",
         "args": ["data-commons-search"],
         "env": {
            "OPENSEARCH_URL": "OPENSEARCH_URL"
         }
      }
   }
}

Or using local folder for development:

{
   "servers": {
      "data-commons-search": {
         "type": "stdio",
         "cwd": "~/dev/data-commons-search",
         "env": {
            "OPENSEARCH_URL": "OPENSEARCH_URL"
         },
         "command": "uv",
         "args": ["run", "data-commons-search"]
      }
   }
}

🛠️ Development

[!IMPORTANT]

Requirements:

uv, to easily handle scripts and virtual environments

docker, to deploy the OpenSearch service (or just access to a running instance)

API key for a LLM provider: e-infra CZ, Mistral.ai, or OpenRouter

📥 Install dev dependencies

uv sync --extra agent

Install pre-commit hooks:

uv run pre-commit install

Create a keys.env file with your LLM provider API key(s):

EINFRACZ_API_KEY=YOUR_API_KEY
MISTRAL_API_KEY=YOUR_API_KEY
OPENROUTER_API_KEY=YOUR_API_KEY

⚡️ Start dev server

Start the server in dev at http://localhost:8000, with MCP endpoint at http://localhost:8000/mcp

uv run uvicorn src.data_commons_search.main:app --log-config logging.yml --reload

Default OPENSEARCH_URL=http://localhost:9200

Customize server configuration through environment variables:

SERVER_PORT=8001 OPENSEARCH_URL=http://localhost:9200 uv run uvicorn src.data_commons_search.main:app --host 0.0.0.0 --port 8001 --log-config logging.yml --reload

[!TIP]

Example curl request:
curl -X POST http://localhost:8000/chat \
	-H "Content-Type: application/json" -H "Authorization: SECRET_KEY" \
	-d '{"messages": [{"role": "user", "content": "Educational datasets from Switzerland covering student assessments, language competencies, and learning outcomes, including experimental or longitudinal studies on pupils or students."}], "model": "einfracz/qwen3-coder"}'
Recommended model per supported provider:

einfracz/qwen3-coder or einfracz/gpt-oss-120b (smaller, faster)

mistralai/mistral-medium-latest (large is older, and not as good with tool calls)

groq/moonshotai/kimi-k2-instruct

openai/gpt-4.1

[!IMPORTANT]

To build and integrate the frontend web app to the server, from the matchmaker frontend folder run:
npm run build && rm -rf ../data-commons-search/src/data_commons_search/webapp/ && cp -R dist/spa/ ../data-commons-search/src/data_commons_search/webapp/

📦 Build for production

Build binary in dist/

uv build

🐳 Deploy with Docker

Create a keys.env file with the API keys:

EINFRACZ_API_KEY=YOUR_API_KEY
MISTRAL_API_KEY=YOUR_API_KEY
OPENROUTER_API_KEY=YOUR_API_KEY
SEARCH_API_KEY=SECRET_KEY_YOU_CAN_USE_IN_FRONTEND_TO_AVOID_SPAM

[!TIP]

SEARCH_API_KEY can be used to add a layer of protection against bots that might spam the LLM, if not provided no API key will be needed to query the API.

You can use the prebuilt docker image ghcr.io/eosc-data-commons/data-commons-search:main

Example compose.yml:

services:
  mcp:
    image: ghcr.io/eosc-data-commons/data-commons-search:main
    ports:
      - "127.0.0.1:8000:8000"
    environment:
      OPENSEARCH_URL: "http://opensearch:9200"
      EINFRACZ_API_KEY: "${EINFRACZ_API_KEY}"

Build and deploy the service:

docker compose up

[!IMPORTANT]

Current deployment to staging server is done automatically through GitHub Actions at each push to the main branch.

When a push is made the workflow will:

Pull the main branch from the frontend repository

Build the frontend, and add it to src/data_commons_search/webapp

Build the docker image for the server

Publish the docker image as main/latest

The staging infrastructure then automatically pull the latest version of the image and deploys it.

✅ Run tests

[!CAUTION]

You need to first start the server on port 8001 (see start dev server section)

uv run pytest

To display all logs when debugging:

uv run pytest -s

🧹 Format code and type check

uvx ruff format
uvx ruff check --fix
uv run mypy

♻️ Reset the environment

Upgrade uv:

uv self update

Clean uv cache:

uv cache clean

🏷️ Release process

[!IMPORTANT]

Get a PyPI API token at pypi.org/manage/account.

Run the release script providing the version bump: fix, minor, or major

.github/release.sh fix

[!TIP]

Add your PyPI token to your environment, e.g. in ~/.zshrc or ~/.bashrc:
export UV_PUBLISH_TOKEN=YOUR_TOKEN

🤝 Acknowledments

The LLM provider einfracz is a service provided by e-INFRA CZ and operated by CERIT-SC Masaryk University

Computational resources were provided by the e-INFRA CZ project (ID:90254), supported by the Ministry of Education, Youth and Sports of the Czech Republic.