Unstructured

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Set up and interact with your unstructured data processing workflows in [Unstructured Platform](https://unstructured.io)

Source Code

Unstructured API MCP Server

An MCP server implementation for interacting with the Unstructured API. This server provides tools to list sources and workflows.

Available Tools

Tool	Description
`list_sources`	Lists available sources from the Unstructured API.
`get_source_info`	Get detailed information about a specific source connector.
`create_source_connector`	Create a source connector.)
`update_source_connector`	Update an existing source connector by params.
`delete_source_connector`	Delete a source connector by source id.
`list_destinations`	Lists available destinations from the Unstructured API.
`get_destination_info`	Get detailed info about a specific destination connector
`create_destination_connector`	Create a destination connector by params.
`update_destination_connector`	Update an existing destination connector by destination id.
`delete_destination_connector`	Delete a destination connector by destination id.
`list_workflows`	Lists workflows from the Unstructured API.
`get_workflow_info`	Get detailed information about a specific workflow.
`create_workflow`	Create a new workflow with source, destination id, etc.
`run_workflow`	Run a specific workflow with workflow id
`update_workflow`	Update an existing workflow by params.
`delete_workflow`	Delete a specific workflow by id.
`list_jobs`	Lists jobs for a specific workflow from the Unstructured API.
`get_job_info`	Get detailed information about a specific job by job id.
`cancel_job`	Delete a specific job by id.

Below is a list of connectors the

UNS-MCP

server currently supports, please see the full list of source connectors that Unstructured platform supports here and destination list here. We are planning on adding more!

Source	Destination
S3	S3
Azure	Weaviate
Google Drive	Pinecone
OneDrive	AstraDB
Salesforce	MongoDB
Sharepoint	Neo4j
	Databricks Volumes
	Databricks Volumes Delta Table

To use the tool that creates/updates/deletes a connector, the credentials for that specific connector must be defined in your .env file. Below is the list of

credentials

for the connectors we support:

Credential Name	Description
`ANTHROPIC_API_KEY`	required to run the `minimal_client` to interact with our server.
`AWS_KEY` , `AWS_SECRET`	required to create S3 connector via `uns-mcp` server, see how in documentation and here
`WEAVIATE_CLOUD_API_KEY`	required to create Weaviate vector db connector, see how in documentation
`FIRECRAWL_API_KEY`	required to use Firecrawl tools in `external/firecrawl.py` , sign up on Firecrawl and get an API key.
`ASTRA_DB_APPLICATION_TOKEN` , `ASTRA_DB_API_ENDPOINT`	required to create Astradb connector via `uns-mcp` server, see how in documentation
`AZURE_CONNECTION_STRING`	required option 1 to create Azure connector via `uns-mcp` server, see how in documentation
`AZURE_ACCOUNT_NAME` + `AZURE_ACCOUNT_KEY`	required option 2 to create Azure connector via `uns-mcp` server, see how in documentation
`AZURE_ACCOUNT_NAME` + `AZURE_SAS_TOKEN`	required option 3 to create Azure connector via `uns-mcp` server, see how in documentation
`NEO4J_PASSWORD`	required to create Neo4j connector via `uns-mcp` server, see how in documentation
`MONGO_DB_CONNECTION_STRING`	required to create Mongodb connector via `uns-mcp` server, see how in documentation
`GOOGLEDRIVE_SERVICE_ACCOUNT_KEY`	a string value. The original server account key (follow documentation) is in json file, run `base64 < /path/to/google_service_account_key.json` in terminal to get the string value
`DATABRICKS_CLIENT_ID` , `DATABRICKS_CLIENT_SECRET`	required to create Databricks volume/delta table connector via `uns-mcp` server, see how in documentation and here
`ONEDRIVE_CLIENT_ID` , `ONEDRIVE_CLIENT_CRED` , `ONEDRIVE_TENANT_ID`	required to create One Drive connector via `uns-mcp` server, see how in documentation
`PINECONE_API_KEY`	required to create Pinecone vector DB connector via `uns-mcp` server, see how in documentation
`SALESFORCE_CONSUMER_KEY` , `SALESFORCE_PRIVATE_KEY`	required to create salesforce source connector via `uns-mcp` server, see how in documentation
`SHAREPOINT_CLIENT_ID` , `SHAREPOINT_CLIENT_CRED` , `SHAREPOINT_TENANT_ID`	required to create One Drive connector via `uns-mcp` server, see how in documentation
`LOG_LEVEL`	Used to set logging level for our `minimal_client` , e.g. set to ERROR to get everything
`CONFIRM_TOOL_USE`	set to true so that `minimal_client` can confirm execution before each tool call
`DEBUG_API_REQUESTS`	set to true so that `uns_mcp/server.py` can output request parameters for better debugging

Firecrawl Source

Firecrawl is a web crawling API that provides two main capabilities in our MCP:

HTML Content Retrieval: Using
```
invoke_firecrawl_crawlhtml
```
to start crawl jobs and
```
check_crawlhtml_status
```
to monitor them
LLM-Optimized Text Generation: Using
```
invoke_firecrawl_llmtxt
```
to generate text and
```
check_llmtxt_status
```
to retrieve results

How Firecrawl works:

Web Crawling Process:

Starts with a specified URL and analyzes it to identify links
Uses the sitemap if available; otherwise follows links found on the website
Recursively traverses each link to discover all subpages
Gathers content from every visited page, handling JavaScript rendering and rate limits
Jobs can be cancelled with
```
cancel_crawlhtml_job
```
if needed
Use this if you require all the info extracted into raw HTML, Unstructured's workflow cleans it up really well :smile:

LLM Text Generation:

After crawling, extracts clean, meaningful text content from the crawled pages
Generates optimized text formats specifically formatted for large language models
Results are automatically uploaded to the specified S3 location
Note: LLM text generation jobs cannot be cancelled once started. The
```
cancel_llmtxt_job
```
function is provided for consistency but is not currently supported by the Firecrawl API.

Note: A

FIRECRAWL_API_KEY

environment variable must be set to use these functions.

Installation & Configuration

This guide provides step-by-step instructions to set up and configure the UNS_MCP server using Python 3.12 and the

uv

tool.

Prerequisites

Python 3.12+
```
uv
```
for environment management
An API key from Unstructured. You can sign up and obtain your API key here.

Using

uv

(Recommended)

No additional installation is required when using

uvx

as it handles execution. However, if you prefer to install the package directly:

uv pip install uns_mcp

Configure Claude Desktop

For integration with Claude Desktop, add the following content to your

claude_desktop_config.json

Note: The file is located in the

~/Library/Application Support/Claude/

directory.

Using
uvx
Command:

{
   "mcpServers": {
      "UNS_MCP": {
         "command": "uvx",
         "args": ["uns_mcp"],
         "env": {
           "UNSTRUCTURED_API_KEY": "<your-key>"
         }
      }
   }
}

Alternatively, Using Python Package:

{
   "mcpServers": {
      "UNS_MCP": {
         "command": "python",
         "args": ["-m", "uns_mcp"],
         "env": {
           "UNSTRUCTURED_API_KEY": "<your-key>"
         }
      }
   }
}

Using Source Code

Clone the repository.
Install dependencies:
```
uv sync
```
Set your Unstructured API key as an environment variable. Create a .env file in the root directory with the following content:
```
UNSTRUCTURED_API_KEY="YOUR_KEY"
```
Refer to
```
.env.template
```
for the configurable environment variables.

You can now run the server using one of the following methods:

<details> <summary> Using Editable Package Installation </summary> Install as an editable package:

uvx pip install -e .

Update your Claude Desktop config:

{
  "mcpServers": {
    "UNS_MCP": {
      "command": "uvx",
      "args": ["uns_mcp"]
    }
  }
}

Note: Remember to point to the uvx executable in environment where you installed the package

</details> <details> <summary> Using SSE Server Protocol </summary>

Note: Not supported by Claude Desktop.

For SSE protocol, you can debug more easily by decoupling the client and server:

Start the server in one terminal:

uv run python uns_mcp/server.py --host 127.0.0.1 --port 8080
# or
make sse-server

Test the server using a local client in another terminal:

uv run python minimal_client/client.py "http://127.0.0.1:8080/sse"
# or
make sse-client

Note: To stop the services, use

Ctrl+C

on the client first, then the server.

</details> <details> <summary> Using Stdio Server Protocol </summary>

Configure Claude Desktop to use stdio:

{
  "mcpServers": {
    "UNS_MCP": {
      "command": "ABSOLUTE/PATH/TO/.local/bin/uv",
      "args": [
        "--directory",
        "ABSOLUTE/PATH/TO/YOUR-UNS-MCP-REPO/uns_mcp",
        "run",
        "server.py"
      ]
    }
  }
}

Alternatively, run the local client:

uv run python minimal_client/client.py uns_mcp/server.py

</details>

Additional Local Client Configuration

Configure the minimal client using environmental variables:

```
LOG_LEVEL="ERROR"
```
: Set to suppress debug outputs from the LLM, displaying clear messages for users.
```
CONFIRM_TOOL_USE='false'
```
: Disable tool use confirmation before execution. Use with caution, especially during development, as LLM may execute expensive workflows or delete data.

Debugging tools

Anthropic provides

MCP Inspector

tool to debug/test your MCP server. Run the following command to spin up a debugging UI. From there, you will be able to add environment variables (pointing to your local env) on the left pane. Include your personal API key there as env var. Go to

tools

, you can test out the capabilities you add to the MCP server.

mcp dev uns_mcp/server.py

If you need to log request call parameters to

UnstructuredClient

, set the environment variable

DEBUG_API_REQUESTS=false

. The logs are stored in a file with the format

unstructured-client-{date}.log

, which can be examined to debug request call parameters to

UnstructuredClient

functions.

Add terminal access to minimal client

We are going to use @wonderwhy-er/desktop-commander to add terminal access to the minimal client. It is built on the MCP Filesystem Server. Be careful, as the client (also LLM) now has access to private files.

Execute the following command to install the package:

npx @wonderwhy-er/desktop-commander setup

Then start client with extra parameter:

uv run python minimal_client/client.py "http://127.0.0.1:8080/sse" "@wonderwhy-er/desktop-commander"
# or
make sse-client-terminal

Using subset of tools

If your client supports using only subset of tools here are the list of things you should be aware:

```
update_workflow
```
tool has to be loaded in the context together with
```
create_workflow
```
tool, because it contains detailed description on how to create and configure custom node.

Known issues

```
update_workflow
```
- needs to have in context the configuration of the workflow it is updating either by providing it by the user or by calling
```
get_workflow_info
```
tool, as this tool doesn't work as
```
patch
```
applier, it fully replaces the workflow config.

CHANGELOG.md

Any new developed features/fixes/enhancements will be added to CHANGELOG.md. 0.x.x-dev pre-release format is preferred before we bump to a stable version.

Troubleshooting

If you encounter issues with
```
Error: spawn <command> ENOENT
```
it means
```
<command>
```
is not installed or visible in your PATH:
- Make sure to install it and add it to your PATH.
- or provide absolute path to the command in the
```
command
```
  field of your config. So for example replace
```
python
```
  with
```
/opt/miniconda3/bin/python
```