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Agent Studio
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python
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6 months ago
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2 months ago
Catalog Refreshed
Documentation & install
Readme and setup notes from the catalogue, plus a client-ready config you can copy for your MCP host.
Installation
Add the following to your MCP client configuration file.
Configuration
View docs{
"mcpServers": {
"jasonmeverett-cloudera-ai-agent-studio-mcp": {
"command": "uv",
"args": [
"--directory",
"/path/to/cloudera-ai-agent-studio-mcp",
"run",
"serve.py"
],
"env": {
"CDSW_APIV2_KEY": "YOUR-TOKEN-HERE",
"AGENT_STUDIO_DOMAIN": "https://your-studio.CDSW_DOMAIN.cldr.work"
}
}
}
}Agent Studio MCP Server exposes your Agent Studio instance as a set of callable tools so MCP-aware clients can list, inspect, and orchestrate workflows on the fly. It acts as a lightweight bridge that lets you manage workflows, agents, and tasks from a centralized MCP client like Claude Desktop.
How to use
You interact with the Agent Studio MCP Server through an MCP client. Use the available tools to list workflows, fetch details, create new workflows, convert existing workflows to conversational mode, and add managers or agents to workflows. This lets you build, inspect, and modify workflows from a single interface without directly touching Agent Studio.
How to install
Prerequisites: ensure you have the MCP runtime available on your machine and that you can run a local stdio MCP server. You will configure a local MCP entry in your client to point at the Agent Studio runtime.
Step 1. Create a directory for the Agent Studio MCP server files.
Step 2. Place the Agent Studio MCP runtime scripts in that directory so you can reference serve.py from the working directory. If you already have the files, skip this step.
Step 3. In your client configuration, define the MCP server as shown below. This enables the client to start the bridge locally and communicate with Agent Studio.
Step 4. Start the MCP server via the runtime command shown in the example configuration.
Additional content
Configuration and environment variables: You must provide environment variables that authenticate and configure your Agent Studio domain. The example below shows two required environment variables: CDSW_APIV2_KEY for API authentication and AGENT_STUDIO_DOMAIN to specify your Agent Studio domain.
{
"mcpServers": {
"agent-studio": {
"type": "stdio",
"command": "uv",
"args": [
"--directory",
"/path/to/cloudera-ai-agent-studio-mcp",
"run",
"serve.py"
],
"env": {
"CDSW_APIV2_KEY": "YOUR-TOKEN-HERE",
"AGENT_STUDIO_DOMAIN": "https://your-studio.CDSW_DOMAIN.cldr.work"
}
}
}
}
Available tools
list_current_workflows
Returns every workflow’s ID and name to populate a UI dropdown of existing projects.
get_workflow_information
Fetches full JSON metadata for a single workflow to inspect tasks, agents, and process settings.
create_workflow
Creates a brand-new blank workflow with a given name and description to automate project scaffolding.
make_workflow_conversational
Adds a Conversational Task to a workflow and marks it as conversational, enabling a chat-first experience.
add_manager_agent_to_workflow
Creates a manager agent and wires it as the workflow’s supervisor for hierarchical workflows.
add_agent_to_workflow
Adds one or more agents to a workflow to expand the team with domain specialists.