Dpf-it

Install

Works with Claude Code, VS Code, Cursor, Windsurf. Read the repository before you give any tool access to your data.

npx agentmarket install connector/dpf-it-com-mcp-server

About

No reviews yet. Installed it? Reviews come from verified installers only. How reviews work

Permissions

What this server can do and what it asks for, from its tool list and install config. Checked Oct 25, 2026.

Tools

5 read · 9 write · 4 delete

Secrets

None asked for

Install scripts

Nothing to install (hosted)

Runs as

Hosted (remote endpoint)

Tools (18)

As the server listed them on Oct 25, 2026. Descriptions are the server’s own words; hidden characters are shown as [U+…].

• call_dpf_apiCall any DPF API action (fallback for requests with no dedicated tool)can change or delete data

  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_workspaces, create_workspace — and reach for this only when none of those fit (e.g. "how many credits do I have?" -> path "/auth/billing", action "get-balance"; a brand-new action added to the API since this server's tools were last updated). Every DPF endpoint is POST <path> with a JSON body of { action,…

• contactSend a message to the DPF team

  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_workspaceCreate a workspace

  Create a new workspace, owned by the authenticated user. Use this if list_my_workspaces returns none.

• delete_data_specDelete a data speccan change or delete data

  Permanently delete a data spec and its associated configuration.

• finish_data_jobRun a data processing job, step 2: start processing after uploading

  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 reaches a terminal status.

• finish_data_source_onboardingOnboard a new data source, step 2: run analysis after uploading

  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, spends no credits, and returns a fresh upload URL plus the exact curl command — complete the upload and call this again. Calling it in the hope that analysis will work without the upload cannot succeed; if you have no filesystem access, ask the user to attach the file and upload it first. If it …

• finish_data_spec_updateUpdate an existing data spec, step 2: run analysis after uploading

  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 hands back a fresh upload URL with the curl command to use. If it returns before the spec settles (timedOut: true), do NOT call this tool again just to keep checking — that re-attempts starting analysis. Poll with get_status (specId) instead until it reaches a terminal status. Upload EVERY file update_…

• get_statusGet spec or job statusread-only

  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-analysis, poll by specId; once that reaches "ready", its response's lastJobId (if present) points at the data-load job — poll that separately by jobId for load progress.

• list_dataList data specs or jobsread-only

  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/page, max 100, newest first); pass the returned nextCursor to fetch more. A spec row also carries its stored input set — sourceType, sampleFileName, formatFileName, targetOption/targetTables/targetSchemaFileName, sourceTables, merge and additionalPrompt — which is exactly what AI code generation re-r…

• list_my_workspacesList my workspacesread-only

  List every workspace the authenticated user has access to, including their permission on each.

• manage_accountGet instructions for DPF account signup, email verification, or password resetread-only

  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 returns the DPF website's own form, or a curl command that reads the password from a shell variable the user sets themselves in their own terminal. Hand the command to the user to run — do not run it yourself even if you have shell access, since composing the export line would require seeing the pass…

• manage_connectionManage an external data-source connection (SFTP, AWS S3)can change or delete data

  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) will succeed. For aws_s3, create generates an ExternalId and returns a trustPolicy plus dpfPrincipalArn — the customer must create (or update) the IAM role at roleArn with that trust policy and a permissions policy granting the S3 access DPF needs, before test will succeed. Either type must pass t…

• manage_triggerManage a workspace trigger (SFTP/AWS S3 pull, spec chaining, or schedule)can change or delete data

  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 requires s3Bucket (s3Prefix optional). Natural-language preRules (which files to pick up) and postRules (what to do after upload) are compiled into executable code server-side — never pass raw code. The connection must already exist and have passed test (see manage_connection). For a first-time "se…

• onboard_data_sourceOnboard a new data source, step 1: create a data spec and get upload URL(s)

  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 user attach it so your platform can), confirm the PUT returned HTTP 200, and only then call finish_data_source_onboarding with the returned specId to kick off AI analysis and wait for it to complete. finish_data_source_onboarding checks storage and refuses if the upload never landed, so skipping the…

• run_data_jobRun a data processing job, step 1: create job and get upload URL(s)

  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 NOT call this right after onboard_data_source/finish_data_source_onboarding or update_data_spec/finish_data_spec_update unless loadSampleData was explicitly set to false there — by default those already load and process the sample file as their own job (see the returned lastJobId), so calling run_dat…

Compare and alternatives

Featured in

• Best MCP servers for data engineering

Signals

Last release

Jul 26, 2026 (3 months ago)

Details

Registry name

com.dpf-it/mcp-server

Version

1.0.0

Auth

Auth not stated

License

License unknown

First seen

Oct 24, 2026

Last checked

Oct 25, 2026

Source

Official MCP Registry