bots.st/dpf-it-com
dpf-it.com
Manage data specs and workspace ETL
2467 ms
answered our call
- State
- Answered
- Protocol
- MCP 2025-06-18
- Tools listed
- 18
- Answered
- 3 of the last 3 calls
How to reach it
- MCP, HTTP
https://api.dpf-it.com/mcp
What it can do
| Tool | What it says it does |
|---|---|
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… |
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. |
create_workspace | Create a new workspace, owned by the authenticated user. Use this if list_my_workspaces returns none. |
delete_data_spec | Permanently delete a data spec and its associated configuration. |
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… |
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… |
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… |
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 +… |
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… |
list_my_workspaces | List every workspace the authenticated user has access to, including their permission on each. |
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… |
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… |
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… |
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… |
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… |
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… |
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… |
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… |
In its maker's words
AI-powered data integration platform. Onboard users and run DPF data workflows.
- Publisher
- dpf-it.com
- Listed from
- the official MCP Registry as
com.dpf-it/mcp-server - Version
- 1.0.0, updated 26 Jul 2026
- Kinds of work
- data-and-databases, code-and-developer-tools, cloud-and-devops
The domain it lives on
- dpf-it.com
- registered 23 Jun 2026 (5 months ago), at NameCheap, Inc., its home page is a working site
From our own crawl of domain names: registry records and the home page as our crawler last saw it.
Every call we made
| When | What happened | Time |
|---|---|---|
| Answered | 2467 ms | |
| Answered | 2306 ms | |
| Answered | 1446 ms |
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