com.dpf-it/mcp-server
com.dpf-it/mcp-serverAI-powered data integration platform. Onboard users and run DPF data workflows.
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Tools · 18
List every workspace the authenticated user has access to, including their permission on each.
Create a new workspace, owned by the authenticated user. Use this if list_my_workspaces returns none.
List either the data specs (parsing + mapping rule sets, resource: "specs") or the data processing jobs (executions of a spec, resource: "jobs") defined in a workspace. Each spec includes its specId a…
Poll the status of either a data spec's own process (schema inference + code generation, run by start-analysis — pass specId, reaches "ready" or "failed") or a data-load job (pass jobId, reaches "comp…
Permanently delete a data spec and its associated configuration.
Run a SQL query against the Iceberg tables loaded into a workspace. To list the tables that actually exist in the workspace, run `SHOW TABLES` — this is the authoritative source (unlike list_data's sp…
Create, list, test, or delete a workspace connection to an external data source. Two types are supported: "sftp" and "aws_s3". For sftp, create generates a keypair and returns the public key — it must…
Create, list, update, delete, or fire a workspace job trigger. Four types: - "sftp"/"aws_s3": pulls files from a connection (sftp: remote server; aws_s3: S3 bucket/prefix) into an already-analyzed dat…
First step of setting up a new data integration: creates a data spec. By default (sourceType "file") this returns presigned upload URL(s) for the sample file (and optional format/target-schema file). …
Call ONLY after the file(s) returned by onboard_data_source have actually been uploaded (each PUT returned HTTP 200) — kicks off AI analysis and waits until the spec reaches "ready" or "failed". This…
Change an existing data spec's configuration. If no replacement file names are given, this runs synchronously (no upload needed): saves changes and — by default — re-runs AI analysis, returning the fi…
Call ONLY after the file(s) returned by update_data_spec have actually been uploaded (each PUT returned HTTP 200) — kicks off AI analysis and waits until the spec reaches "ready" or "failed". The uplo…
First step of processing new data files through an already-configured data spec: creates a job and returns presigned upload URL(s) for each file. Upload the file(s) per the returned instructions, then…
Call after uploading the file(s) returned by run_data_job — starts processing and waits until the job completes or fails. If it returns before that (timedOut: true), do NOT call this tool again just t…
End-to-end workflow for "pull files from this SFTP server / S3 bucket on a schedule" requests: reuses a matching connection if one already exists in the workspace (same hostname/username for sftp, sam…
Escape hatch for DPF capabilities that don't have a dedicated tool yet. ALWAYS prefer a dedicated tool when one exists — get_status, list_data, submit_query, delete_data_spec, onboard_data_source, upd…
Returns instructions for creating a DPF account, verifying its email, resending the verification code, or resetting a forgotten password — it never performs these itself and never asks for a password.…
Send a message to the DPF team — request a demo, ask about licensing, report an issue, or request a feature. No authentication required. Always ask the user for their email if they have not already gi…
How to use
Add to your Claude Desktop / Cursor / Cline MCP config:
{
"mcpServers": {
"com.dpf-it/mcp-server": {
"url": "https://api.dpf-it.com/mcp",
"transport": "streamable-http"
}
}
}