DataPrem MCP Server

A Model Context Protocol (MCP) server that exposes Spanish public-data sources to AI agents (Claude Desktop, Cursor, ChatGPT, …).

It is a thin client of the DataPrem REST API: each MCP tool maps to an HTTPS call against api.dataprem.com using your API key.

Requires the MCP Python SDK 2.x (mcp>=2.0.0,<3).

Tool status (0.4.0)

Planned tools are in the catalogue so an agent can discover them, and they call the API like every other tool. Until a connector ships the API answers not_implemented, so no tool ever returns data that is not real.

Related MCP server: eRegulations MCP Server

Getting an API key

Every tool requires a Bearer token from api.dataprem.com:

1. Request access by email to info@dataprem.com, describing your use case.

2. You will receive a token prefixed dpa_… along with the API URL.

3. Configure it in your MCP client (next section).

Installation

Requires Python 3.11+.

# Via PyPI (recommended for MCP clients)
uvx dataprem-mcp

# Or local install for development
pip install -e ".[dev]"

Claude Desktop configuration

Edit claude_desktop_config.json (Mac: ~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "dataprem": {
      "command": "uvx",
      "args": ["dataprem-mcp"],
      "env": {
        "DATAPREM_API_KEY": "dpa_YOUR_TOKEN_HERE",
        "DATAPREM_API_URL": "https://api.dataprem.com"
      }
    }
  }
}

Restart Claude Desktop. The four tools should show up as available to the model.

Server-side HTTP transport

For clients that cannot spawn the server as a subprocess (e.g. a multi-request web app) there is a streamable-http transport that runs the server as a long-lived process listening for JSON-RPC over HTTP.

# Without Docker
python -m dataprem_mcp --transport streamable-http --host 0.0.0.0 --port 8080

# With Docker
docker compose up dataprem_mcp     # local image build; exposed only on the internal network

The MCP endpoint is /mcp (no trailing slash). The standard handshake (initialize → tools/list → tools/call) works with Content-Type: application/json and Accept: application/json, text/event-stream. Each conversation receives an mcp-session-id that the client must echo back on subsequent requests.

# Example: initialize handshake
curl -sL -X POST http://127.0.0.1:8080/mcp \
  -H 'Content-Type: application/json' \
  -H 'Accept: application/json, text/event-stream' \
  -d '{
    "jsonrpc": "2.0",
    "id": 1,
    "method": "initialize",
    "params": {
      "protocolVersion": "2025-03-26",
      "clientInfo": {"name": "smoke", "version": "1"},
      "capabilities": {}
    }
  }' -i

By default the compose service does not publish the port to the host: place it on a Docker network shared with your client and reach it as http://dataprem_mcp:8080/mcp.

Alternative configuration (local development)

{
  "mcpServers": {
    "dataprem-dev": {
      "command": "python",
      "args": ["-m", "dataprem_mcp"],
      "cwd": "/path/to/dataprem-mcp",
      "env": {
        "DATAPREM_API_KEY": "dpa_dev_token",
        "DATAPREM_API_URL": "http://localhost:8000"
      }
    }
  }
}

Environment variables

Tools — reference

dataprem_catastro_lookup ✅ Live

Looks up cadastral data for a property. Two modes are supported:

By cadastral reference:

By address:

Returns the normalised cadastral record (class, use, surfaces, year of construction, address with INE codes, breakdown of constructions by floor and use). Does not expose the owner for LOPD/GDPR reasons.

dataprem_borme_search (planned)

dataprem_cendoj_search (planned)

dataprem_tenders_search (planned)

Response shape

Every tool returns a dict marking success or failure with ok:

{ "ok": true, "data": { ... cadastral record ... } }

{ "ok": false, "error": "unauthorized", "message": "API key invalid or revoked..." }

A planned source answers without data:

{ "ok": false, "error": "not_implemented", "message": "The borme source is not available yet.",
  "source": "borme" }

Error codes:

Development

# Install dev dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Start the installed console script over stdio and list its tools,
# the same way a desktop client does. The suite alone cannot catch a
# package that imports fine from the source tree but not once installed.
python scripts/smoke_stdio.py

# Run the server with a local API key
DATAPREM_API_KEY=dpa_xxx DATAPREM_API_URL=http://localhost python -m dataprem_mcp

Releasing

Publishing runs from CI through PyPI trusted publishing, so no token lives on anyone's machine:

# bump the version in pyproject.toml and add the CHANGELOG entry, then
git tag 0.3.4
git push origin 0.3.4

The release workflow builds, checks the artifact, installs the wheel, starts it over stdio, refuses to continue if the tag disagrees with the built version, and only then publishes.

License

MIT