Conductor MCP Server
Enables AI agents to create, execute, and analyze workflows on a Conductor instance through natural language.
README
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Conductor mcp server
Model Context Protocol server for Conductor.
This package is used to run an MCP server that is capable of interacting with a Conductor instance. It provides tools for the basic operations that may be needed by an MCP client for Workflow creation, execution, and analysis.
PyPi Quickstart
Install package
pip install conductor-mcp
Create a JSON config with your Conductor keys
{
"CONDUCTOR_SERVER_URL": "https://developer.orkescloud.com/api",
"CONDUCTOR_AUTH_KEY": "<YOUR_APPLICATION_AUTH_KEY>",
"CONDUCTOR_AUTH_SECRET": "<YOUR_APPLICATION_SECRET_KEY>"
}
Note: the
/apipath is required as part of the CONDUCTOR_SERVER_URL for most applications
Plug the server into an AI Agent, such as Claude or Cursor
{
"mcpServers": {
"conductor": {
"command": "conductor-mcp",
"args": [
"--config",
"<ABSOLUTE PATH TO A JSON CONFIG FILE>"
]
}
}
}
You should now be able to interact with Conductor via your AI Agent.
Adding to Claude
You can find instructions for adding to Claude here.
In general, you just add the mcpServers config (above) to your Claude config (or create it if it doesn't exist). For
instance, on Mac it might be ~/Library/Application\ Support/Claude/claude_desktop_config.json.
Adding to Cursor
The main Cursor instructions are here.
Go to Cursor -> Settings -> Cursor Settings -> MCP and select "+ Add new global MCP server".
Here you can add the exact same configuration file shown in the example for Claude (above). You can then access the AI chat feature and explore the MCP server in the sidebar with ⌘+L (Mac) or Ctrl+L (Windows/Linux).
Example prompts
Get Flight Risk Info
Create and execute a Conductor Workflow that calls any necessary http endpoints to gather current weather data around
Seattle and outputs the risk factors for flying a small airplane around the South Lake Union area using Visual Flight
Rules today. Only use publicly available endpoints that don't require an API key.
Notify Stocks
(May require API Keys)
Create a Conductor Workflow that runs on a daily schedule, accepts a list of email address and a stock symbol, checks
current stock prices, and sends an email to everyone on the list if they should be happy or sad today based on stock
performance. Name the workflow "NotifyStonks" and use schemaVersion 2.
GitHub Quickstart
Clone GitHub Repo
gh repo clone conductor-oss/conductor-mcp
This project relies on uv https://docs.astral.sh/uv/getting-started/
Create venv
(not entirely necessary, since uv automatically creates and uses the virtual environment on its own when running other commands)
uv sync
source .venv/bin/activate
Define Env Vars
You can continue to use a JSON config file and the --config flag, or if the server is running in an environment where
you have control over the environment variables the MCP server will look for them there if a config file is not
provided.
export CONDUCTOR_SERVER_URL="YOUR_CONDUCTOR_SERVER_URL"
export CONDUCTOR_AUTH_KEY="<YOUR_APPLICATION_AUTH_KEY>"
export CONDUCTOR_AUTH_SECRET="<YOUR_APPLICATION_SECRET_KEY>"
Configure Your AI Assistant
{
"mcpServers": {
"conductor": {
"command": "uv",
"args": [
"--directory",
"<ABSOLUTE_PATH_TO_THE_PROJECT>",
"run",
"conductor-mcp",
"--config",
"<ABSOLUTE PATH TO A JSON CONFIG FILE>"
]
}
}
}
Or Run Server Directly
cd <PROJECT_ROOT>
uv run conductor-mcp --config <ABSOLUTE PATH TO A JSON CONFIG FILE>
Note: a
local_development.pyalso exists for setting env vars and will be used when the--local_devflag is set.
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