@dpf-it/mcp-server

MCP (Model Context Protocol) server for DPF — an AI-driven data integration platform built API-first, with every action available through both the REST API and this MCP server. This server lets AI agents and chat clients discover DPF, register new accounts, connect existing workspaces, and run end-to-end data integration workflows without leaving the conversation.

Quick start

No account or credentials required to begin — just add the server to your MCP client config:

{
  "mcpServers": {
    "dpf": {
      "command": "npx",
      "args": ["-y", "@dpf-it/mcp-server"]
    }
  }
}

Then in a chat session, ask the agent to "get started with DPF." It will walk you through registering an account (or connecting an existing workspace) and remembers your connection for future sessions.

How authentication works

DPF workspaces issue OAuth2 client_credentials pairs (clientId / clientSecret) from the workspace settings page (or via the login_and_create_workspace / connect_workspace tools below). This server:

1. Stores those credentials locally at ~/.dpf/credentials.json (file permissions 0600), keyed by workspace.
2. Exchanges them for short-lived bearer JWTs on demand via DPF's OAuth token endpoint, caching tokens in memory for the life of the process.
3. Re-reads the credentials file on every startup, so once a workspace is connected, future sessions/chats need no further login.

You can connect multiple workspaces; tools accept an optional workspaceId and default to the first connected one if omitted.

Tools

Bootstrap (no auth required)

• get_started — explains DPF and reports which workspaces are already connected
• register_account — create a brand-new DPF account
• login_and_create_workspace — log in, create a workspace, and persist its credentials
• connect_workspace — connect an existing workspace using a clientId/clientSecret from the DPF portal
• list_workspaces — list locally connected workspaces

Data operations

• list_data_specs, list_jobs, get_job_status, delete_data_spec, submit_query

Skills (multi-step workflows)

• onboard_data_source — create a data spec, upload sample/format files, run AI analysis, and wait for it to complete
• run_data_job — create a job for an existing spec, upload data files, start processing, and wait for completion

Configuration

| Env var | Default | Purpose | |---|---|---| | DPF_API_BASE_URL | https://api.dpf-it.com | DPF REST API base URL | | DPF_OAUTH_TOKEN_URL | ${DPF_API_BASE_URL}/iceberg/v1/oauth/tokens | OAuth2 client_credentials token endpoint |

License

MIT