MCP Server is an MCP server with 18 tools an agent can call, among them call_dpf_api, contact, create_workspace. It answers at https://api.dpf-it.com/mcp over streamable HTTP, and it listed its tools to an anonymous client, so no sign-in was needed for that. Our probes reached it 10 of 10 times since 4 September 2026; the last check, on 18 October 2026, succeeded.
AI-powered data integration platform. Onboard users and run DPF data workflows.
Description as the publisher wrote it.
| Tool | What 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_conn Inputs: path, action, workspaceId, params |
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. Inputs: reason, message, email, name |
create_workspace | Create a new workspace, owned by the authenticated user. Use this if list_my_workspaces returns none. Inputs: name, description |
delete_data_spec | Permanently delete a data spec and its associated configuration. Inputs: workspaceId, specName |
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 start Inputs: workspaceId, jobId, specName |
finish_data_source_onboarding | Call after uploading the file(s) returned by onboard_data_source — kicks off AI analysis and waits until the spec reaches "ready" or "failed". If it returns before that (timedOut: true), do NOT call this tool again just to keep checking — t Inputs: workspaceId, specId, specName, loadSampleData |
finish_data_spec_update | Call after uploading the file(s) returned by update_data_spec — kicks off AI analysis and waits until the spec reaches "ready" or "failed". If it returns before that (timedOut: true), do NOT call this tool again just to keep checking — that Inputs: workspaceId, specId, specName, runAnalysis, loadSampleData |
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 Inputs: workspaceId, specId, jobId |
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 Inputs: workspaceId, resource, pageSize, cursor |
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 s Inputs: action, email, firstName, lastName, termsAccepted, otp |
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 aut Inputs: workspaceId, action, type, hostname, username, roleArn |
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/month Inputs: workspaceId, action, triggerId, specName, connectionId, type |
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) — upload the file(s) per the returned ins Inputs: workspaceId, specName, description, sourceType, sourceTables, targetOption |
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 Inputs: workspaceId, specName, fileNames |
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 Inputs: workspaceId, hostname, username, roleArn, s3Bucket, s3Prefix |
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 Inputs: workspaceId, sql |
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 sa Inputs: workspaceId, specName, description, targetOption, targetTables, targetSchemaFileName |
Every block below points at https://api.dpf-it.com/mcp. Pick your client and copy.
claude mcp add --transport http mcp-server https://api.dpf-it.com/mcp
{
"mcpServers": {
"mcp-server": {
"command": "npx",
"args": ["-y", "mcp-remote", "https://api.dpf-it.com/mcp"]
}
}
}{
"mcpServers": {
"mcp-server": {
"url": "https://api.dpf-it.com/mcp"
}
}
}{
"servers": {
"mcp-server": {
"type": "http",
"url": "https://api.dpf-it.com/mcp"
}
}
}{
"context_servers": {
"mcp-server": {
"url": "https://api.dpf-it.com/mcp"
}
}
}{
"mcpServers": {
"mcp-server": {
"serverUrl": "https://api.dpf-it.com/mcp"
}
}
}{
"mcpServers": {
"mcp-server": {
"url": "https://api.dpf-it.com/mcp"
}
}
}name: MCP Server
version: 0.0.1
schema: v1
mcpServers:
- name: MCP Server
type: streamable-http
url: https://api.dpf-it.com/mcp{
"mcpServers": {
"mcp-server": {
"httpUrl": "https://api.dpf-it.com/mcp"
}
}
}{
"mcpServers": {
"mcp-server": {
"type": "http",
"url": "https://api.dpf-it.com/mcp"
}
}
}{
"mcpServers": {
"mcp-server": {
"url": "https://api.dpf-it.com/mcp"
}
}
}goose://extension?url=https%3A%2F%2Fapi.dpf-it.com%2Fmcp&type=streamable_http&id=mcp-server&name=MCP%20Server
It answered our last check on 18 October 2026 in 358 ms. Over 10 checks since 4 September 2026 it answered 10. Status here comes from Neuronto's own probes, not from the publisher.
Yes. It is published by dpf-it.com and served from api.dpf-it.com, the vendor's own domain: https://api.dpf-it.com/mcp.
18, as returned by the server's own tools/list on 10 September 2026: call_dpf_api, contact, create_workspace, delete_data_spec, finish_data_job, finish_data_source_onboarding, finish_data_spec_update, get_status, and 10 more.
It answered our last check on 18 October 2026 in 358 ms. Over 10 checks since 4 September 2026 it answered 10.
Put the server under mcpServers in ~/.cursor/mcp.json with the URL https://api.dpf-it.com/mcp. The exact JSON is above, with the same setup for Claude Code, Claude Desktop, VS Code, Windsurf, Zed, Gemini CLI and the rest.
It listed its tools to an anonymous client, so reading the tool list needs no key. Individual tools can still ask for credentials in their own inputs.
Publisher: com.dpf-it. Endpoint host: api.dpf-it.com. ARD identifier: urn:air:com.dpf-it:mcp:mcp-server.
First indexed 31 August 2026. The tools are exactly what the server returned; nothing on this page
is inferred from its name. Ranking in Neuronto search is never for sale.