gumloop mcp
Enables AI models to search the web for current information before generating responses, with features for conditional searching, geographic customization, and automatic citations.
Enables AI models to search the web for current information before generating responses, with features for conditional searching, geographic customization, and automatic citations.
MCP Server for Gumloop s API, enabling AI models to manage and execute automations through a standardized interface.
startAutomation
Initiates a new flow run for a specific saved automation.
Inputs:
- user_id
(string): The ID for the user initiating the flow
- saved_item_id
(string): The ID for the saved flow
- project_id
(string, optional): The ID of the project within which the flow is executed
- pipeline_inputs
(array, optional): List of inputs for the flow
- input_name
(string): The input_name parameter from your Input node
- value
(string): The value to be passed to the Input node
Returns: Response with run details including run_id, saved_item_id, workbook_id and URL
retrieveRunDetails
Retrieves details about a specific flow run.
Inputs:
- run_id
(string): ID of the flow run to retrieve
- user_id
(string, optional): The ID for the user initiating the flow
- project_id
(string, optional): The ID of the project within which the flow is executed
Returns: Response with run details including state, outputs, timestamps, and logs
listSavedFlows
Retrieves a list of all saved flows for a user or project.
Inputs:
- user_id
(string, optional): The user ID for which to list items
- project_id
(string, optional): The project ID for which to list items
Returns: Response with list of saved flows and their metadata
listWorkbooks
Retrieves a list of all workbooks and their associated saved flows.
Inputs:
- user_id
(string, optional): The user ID for which to list workbooks
- project_id
(string, optional): The project ID for which to list workbooks
Returns: Response with list of workbooks and their associated saved flows
retrieveInputSchema
Retrieves the input schema for a specific saved flow.
Inputs:
- saved_item_id
(string): The ID of the saved item for which to retrieve input schemas
- user_id
(string, optional): User ID that created the flow
- project_id
(string, optional): Project ID that the flow is under
Returns: Response with list of input parameters for the flow
uploadFile
Uploads a single file to the Gumloop platform.
Inputs:
- file_name
(string): The name of the file to be uploaded
- file_content
(string): Base64 encoded content of the file
- user_id
(string, optional): The user ID associated with the file
- project_id
(string, optional): The project ID associated with the file
Returns: Response with success status and file name
uploadMultipleFiles
Uploads multiple files to the Gumloop platform in a single request.
Inputs:
- files
(array): Array of file objects to upload
- file_name
(string): The name of the file to be uploaded
- file_content
(string): Base64 encoded content of the file
- user_id
(string, optional): The user ID associated with the files
- project_id
(string, optional): The project ID associated with the files
Returns: Response with success status and list of uploaded file names
downloadFile
Downloads a specific file from the Gumloop platform.
Inputs:
- file_name
(string): The name of the file to download
- run_id
(string): The ID of the flow run associated with the file
- saved_item_id
(string): The saved item ID associated with the file
- user_id
(string, optional): The user ID associated with the flow run
- project_id
(string, optional): The project ID associated with the flow run
Returns: The requested file content
downloadMultipleFiles
Downloads multiple files from the Gumloop platform as a zip archive.
Inputs:
- file_names
(array): An array of file names to download
- run_id
(string): The ID of the flow run associated with the files
- user_id
(string, optional): The user ID associated with the files
- project_id
(string, optional): The project ID associated with the files
- saved_item_id
(string, optional): The saved item ID associated with the files
Returns: Zip file containing the requested files
Create a Gumloop API key with access to the required features:
To use this with Claude Desktop, add the following to your claude_desktop_config.json
:
{
"mcpServers": {
"gumloop": {
"command": "npx",
"args": [
"-y",
"gumloop-mcp-server"
],
"env": {
"GUMLOOP_API_KEY": "<YOUR_GUMLOOP_API_KEY>"
}
}
}
}
{
"mcpServers": {
"gumloop": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"-e",
"GUMLOOP_API_KEY",
"gumloop-mcp-server"
],
"env": {
"GUMLOOP_API_KEY": "<YOUR_GUMLOOP_API_KEY>"
}
}
}
}
// Start a saved automation flow
const result = await agent.callTool("startAutomation", {
user_id: "user123",
saved_item_id: "flow456",
pipeline_inputs: [
{
input_name: "search_query",
value: "AI automation trends 2025"
}
]
});
// Check the status of a running automation
const result = await agent.callTool("retrieveRunDetails", {
run_id: "run789",
user_id: "user123"
});
// Get all saved flows for a user
const result = await agent.callTool("listSavedFlows", {
user_id: "user123"
});
// Upload a file to be used in an automation
const result = await agent.callTool("uploadFile", {
user_id: "user123",
file_name: "data.csv",
file_content: "base64EncodedFileContent..."
});
The server returns Gumloop API responses in JSON format. Here s an example for retrieving run details:
{
"user_id": "user123",
"state": "RUNNING",
"outputs": {},
"created_ts": "2023-11-07T05:31:56Z",
"finished_ts": null,
"log": [
"Starting automation flow...",
"Processing input parameters...",
"Executing node 1: Web Scraper..."
]
}
# Install dependencies
pnpm install
# Build the project
pnpm run build
# Start the server
pnpm start
This MCP server is licensed under the MIT License.