list_sourcesLABSCO SUMMARY
This one operates the pipeline rather than processing documents: connectors, workflows, jobs.
Everything here is about the plumbing — point a source at a destination, run the workflow, then check on the job — which is what you want when the ingestion into a vector database has to be set up or re-run rather than debugged one file at a time. Note the repository's own header: it is kept for reference, and Unstructured directs parsing work to its hosted Transform server instead.
What it is
A control-plane server for the Unstructured API: it manages source connectors, destination connectors, workflows and the jobs they produce. The repository states it is no longer actively maintained and points to the hosted Unstructured Transform server for parsing files.
What you get
- Source connectors listed, inspected, created, updated and deleted
- Destination connectors handled the same way, covering vector databases and storage targets
- Workflows created against a source and destination, updated, run on demand and deleted
- Jobs listed per workflow, inspected individually and cancelled
- A listing of workflows that have completed at least one job, with the source and destination details attached
Requirements
An Unstructured API key from platform.unstructured.io, set as UNSTRUCTURED_API_KEY, and Python 3.12+ with uv. Published on PyPI as uns_mcp and run with uvx over stdio. Each connector you create needs that service's own credentials in the environment — AWS_KEY and AWS_SECRET for S3, PINECONE_API_KEY for Pinecone, ASTRA_DB_APPLICATION_TOKEN with its endpoint for Astra, and so on for Azure, Databricks, OneDrive, SharePoint, Neo4j, MongoDB, Weaviate, Google Drive and Salesforce.
Setup effort
One command plus a key — uv pip install uns_mcp, then supply credentials
USE CASES
01
Setting up an ingestion path end to end
Create the source connector, create the destination, then build the workflow that joins them.
02
Re-running ingestion after the source changed
Find the workflow, run it, then watch the job it produced.
03
Working out why nothing arrived downstream
Read the workflow's configuration and the destination connector it writes to, then check the job status.
04
Stopping a run that should not have started
Cancel the job by id rather than letting it finish.
05
Auditing what has actually completed
List the workflows with finished jobs and see their sources and destinations in one place.
20 tools
Listed in project docs. Your client may show a different list.
get_source_infocreate_source_connectorupdate_source_connectordelete_source_connectorlist_destinationsget_destination_infocreate_destination_connectorupdate_destination_connectordelete_destination_connectorlist_workflowsget_workflow_infocreate_workflowrun_workflowupdate_workflowdelete_workflowlist_jobsget_job_infocancel_joblist_workflows_with_finished_jobsBEFORE YOU INSTALL
What to know first
Tools you get
20 tools named in the project's documentation.
Requires
- Python 3.12+
- UNSTRUCTURED_API_KEY in your environment
Where it runs
The documented client runs the server on your machine.
INSTALLATION
Connect your client
Server setup
TERMINAL
uv pip install uns_mcpMCP Clients
Claude Desktop — Python package
CLAUDE DESKTOP — PYTHON PACKAGE · JSON
{
"mcpServers": {
"UNS_MCP": {
"command": "python",
"args": [
"-m",
"uns_mcp"
],
"env": {
"UNSTRUCTURED_API_KEY": "<YOUR_UNSTRUCTURED_API_KEY>"
}
}
}
}
