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com.dpf-it/mcp-server

com.dpf-it/mcp-server · v1.0.0

Measured by MCP Station

Description written by the publisher, not verified

AI-powered data integration platform. Onboard users and run DPF data workflows.

Connect

claude mcp add --transport http mcp-server https://api.dpf-it.com/mcp

{
  "mcpServers": {
    "mcp-server": {
      "url": "https://api.dpf-it.com/mcp"
    }
  }
}

Tools as reported by the server

• list_my_workspaces
  List every workspace the authenticated user has access to, including their permission on each.
• create_workspace
  Create a new workspace, owned by the authenticated user. Use this if list_my_workspaces returns none.
• list_data
  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 and current status — poll a specific one with get_status. Both resources are paginated (default 25/pa
• get_status
  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 "complete" or "failed"). Pass exactly one of specId or jobId. Right after create-spec/update-spec + start
• delete_data_spec write/delete
  Permanently delete a data spec and its associated configuration.
• submit_query
  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 specs, which describe pipelines, not live tables). Qualified table references (catalog/schema prefixes
• manage_connection write/delete
  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 be installed in the remote server's authorized_keys before test (or a trigger using this connection
• manage_trigger write/delete
  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 data spec on a schedule (hourly/daily/monthly, UTC). Type must match the connection's type; aws_s3 also
• onboard_data_source
  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). Creating the spec does NOT upload anything: you must PUT each file to its URL yourself (or have the
• finish_data_source_onboarding
  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 tool verifies the upload against storage before starting anything. If a file is not there it refuses
• update_data_spec
  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 final status directly. If a replacement sample/format/target-schema file name IS given, this instead r
• finish_data_spec_update
  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 upload is verified against storage first: if a file is missing this refuses, spends no credits, and hand
• run_data_job
  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 finish_data_job with the returned jobId to start processing and wait for it to complete. Do NO
• finish_data_job
  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 to keep checking — that re-attempts starting the job. Poll with get_status (jobId) instead until it r
• setup_scheduled_pull
  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, same roleArn for aws_s3), otherwise creates one; tests it; then creates a trigger that feeds an already
• call_dpf_api write/delete
  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, update_data_spec, run_data_job, manage_connection, manage_trigger, setup_scheduled_pull, list_my_worksp
• manage_account
  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. A password typed into this chat would sit in the conversation transcript, so every action instead r
• contact
  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 given it in this conversation.

JSON · Text for agents · Registry updated 2026-07-26