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.
`list_workflows_with_finished_jobs`	Lists all workflows that have any completed job, together with information about source and destination details.

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 😄

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:

Using Editable Package Installation

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

Using SSE Server Protocol

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.

Using Stdio Server Protocol

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

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@^0.2.11"
# 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

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.
`list_workflows_with_finished_jobs`	Lists all workflows that have any completed job, together with information about source and destination details.

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

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 😄

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:

Using Editable Package Installation

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

Using SSE Server Protocol

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.

Using Stdio Server Protocol

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

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

mcp dev uns_mcp/server.py

Add terminal access to minimal client

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@^0.2.11"
# 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

Unstructured API MCP Server

Unstructured API MCP Server

Available Tools

Firecrawl Source

Installation & Configuration

Prerequisites

Using uv (Recommended)

Configure Claude Desktop

Using Source Code

Additional Local Client Configuration

Debugging tools

Add terminal access to minimal client

Using subset of tools

Known issues

CHANGELOG.md

Troubleshooting

Unstructured API MCP Server

Available Tools

Firecrawl Source

Installation & Configuration

Prerequisites

Using uv (Recommended)

Configure Claude Desktop

Using Source Code

Additional Local Client Configuration

Debugging tools

Add terminal access to minimal client

Using subset of tools

Known issues

CHANGELOG.md

Troubleshooting

Using `uv` (Recommended)

Using `uv` (Recommended)