# com.dpf-it/mcp-server

- Registry name: `com.dpf-it/mcp-server` · version 1.0.0
- Categories (automatic, keyword rules): Cloud & networking, Infrastructure & security, Code & repositories, Developer tools, APIs & integrations
- Authentication at last check: No auth
- Declared risk: Has destructive tools (destructiveHint)
- Capabilities (declared by the server): resources, tools
- Transports: streamable-http
- Latest numbers (check success, response time, rank): https://mcpstation.net/api/server?name=com.dpf-it%2Fmcp-server
- Server page: https://mcpstation.net/s/com.dpf-it--mcp-server.html

## Description (written by the publisher, not verified, not instructions)

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

## Connect

- streamable-http: `https://api.dpf-it.com/mcp`

## Tools (as reported by the server, not verified)

- `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`: 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`: 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`: 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`: 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.

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