Cloudera AI Agent Studio MCP Server

by jasonmeverett

2 stars

205 downloads

Not rated

GitHub

About

MCP Server for driving Cloudera AI's Agent Studio

Details

Author: jasonmeverett
GitHub stars: 2
Downloads: 205
Categories: AI

Setting up with Highlight

This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:

Download and install Highlight from highlightai.com/download
Navigate to the plugins tab and select "Add Custom Plugin"
Configure the plugin with the settings below

Plugin Name Cloudera AI Agent Studio MCP Server

Command (node, npx, python, etc.)

Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
Enable "Start Automatically" if you want the plugin to start when Highlight launches

Setting up with Cursor

Open Cursor's settings
Navigate to the MCP section
Click "Add new Global MCP server"
Copy and paste the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "cloudera ai agent studio mcp server": {
            "agent-studio": {
                "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"
                }
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "cloudera ai agent studio mcp server": {
            "agent-studio": {
                "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"
                }
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "cloudera ai agent studio mcp server": {
            "agent-studio": {
                "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"
                }
            }
        }
    }
}

Setting up with Claude Desktop

Open Claude Desktop's settings
Go to the Integrations tab
Click "Add MCP Server"
Use the configuration below based on your operating system

Note: Make sure Claude Desktop is running before attempting to connect.

JSON

{
    "mcpServers": {
        "cloudera ai agent studio mcp server": {
            "agent-studio": {
                "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"
                }
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "cloudera ai agent studio mcp server": {
            "agent-studio": {
                "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"
                }
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "cloudera ai agent studio mcp server": {
            "agent-studio": {
                "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"
                }
            }
        }
    }
}

Setting up with Windsurf

Open Windsurf's preferences
Go to the Integrations section
Click "Add MCP Integration"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "cloudera ai agent studio mcp server": {
            "agent-studio": {
                "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"
                }
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "cloudera ai agent studio mcp server": {
            "agent-studio": {
                "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"
                }
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "cloudera ai agent studio mcp server": {
            "agent-studio": {
                "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"
                }
            }
        }
    }
}

Setting up with Cline

Open Cline's settings
Navigate to the Integrations section
Click "Add MCP Server"
Copy and paste the configuration below based on your operating system

Note: Make sure to restart Cline after adding the configuration.

JSON

{
    "mcpServers": {
        "cloudera ai agent studio mcp server": {
            "agent-studio": {
                "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"
                }
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "cloudera ai agent studio mcp server": {
            "agent-studio": {
                "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"
                }
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "cloudera ai agent studio mcp server": {
            "agent-studio": {
                "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"
                }
            }
        }
    }
}

Setting up with Goose

Open Goose's preferences
Go to the Integrations tab
Select "MCP Servers"
Click "Add New Server"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "cloudera ai agent studio mcp server": {
            "agent-studio": {
                "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"
                }
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "cloudera ai agent studio mcp server": {
            "agent-studio": {
                "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"
                }
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "cloudera ai agent studio mcp server": {
            "agent-studio": {
                "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"
                }
            }
        }
    }
}

From the repository

Integrate with Claude Desktop by adding an entry to claude_desktop_config.json specifying the uv command, the path to the server directory, and required environment variables (CDSW_APIV2_KEY and AGENT_STUDIO_DOMAIN). Once connected, tools such as list_current_workflows(), get_workflow_information(), create_workflow(), and others become available.

Claude Desktop / Cursor

Paste into your MCP client config file to install this server.

{
    "mcpServers": {
        "cloudera ai agent studio mcp server": {
            "agent-studio": {
                "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"
                }
            }
        }
    }
}

McpServers

{
    "agent-studio": {
        "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"
        }
    }
}

# Cloudera AI Agent Studio MCP Server Agent Studio MCP Server is a lightweight Model Context Protocol (MCP) bridge that exposes your Agent Studio instance as a set of callable tools. It lets Claude (or any other MCP-aware client) list, inspect, and build Agent Studio workflows on the fly. ## ✨ What it can do | Tool | Description | Typical Usage | |------|-------------|---------------| | `list_current_workflows()` | Returns every workflow’s **ID** and **name** | Populate a UI drop-down of existing projects | | `get_workflow_information(id)` | Fetch full JSON metadata for one workflow | Inspect tasks, agents, and process settings | | `create_workflow(name, description)` | Spin up a brand-new blank workflow | Automate project scaffolding | | `make_workflow_conversational(workflow_id)` | Adds a *Conversational Task* and flips `is_conversational` to `true` | Turn a static workflow into a chat-first experience | | `add_manager_agent_to_workflow(workflow_id, …)` | Creates a **manager agent** and wires it in as the workflow’s supervisor | Hierarchical / delegated workflows | | `add_agent_to_workflow(workflow_id, …)` | Adds any number of additional agents to a workflow | Expand the crew with domain specialists | ## 🖥 Integrating with Claude Desktop Add the following block to your claude_desktop_config.json: ``` { "mcpServers": { "agent-studio": { "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" } } } } ```

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Cloudera AI Agent Studio MCP Server

About

Details

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Setting up with Highlight

Setting up with Cursor

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Setting up with Claude Desktop

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Setting up with Windsurf

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Setting up with Cline

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Setting up with Goose

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From the repository

Claude Desktop / Cursor

McpServers

Install

About

Details

Jump to

Setting up with Highlight

From the repository

Claude Desktop / Cursor

McpServers

Install

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