Documentation
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 " |
}
}
}
}
**Alternatively, Using Python Package:**{
"mcpServers": {
"UNS_MCP": {
"command": "python",
"args": ["-m", "uns_mcp"],
"env": {
"UNSTRUCTURED_API_KEY": ""
}
}
}
}
### Using Source Code
1. Clone the repository.
2. Install dependencies:uv sync
3. 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:
1. Start the server in one terminal:uv run python uns_mcp/server.py --host 127.0.0.1 --port 8080
# or
make sse-server
2. 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](https://github.com/wonderwhy-er/DesktopCommanderMCP) 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 ENOENT` it means `` 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`Similar MCP
Based on tags & features
Trending MCP
Most active this week