Systemonomic MCP Servers
MCP (Model Context Protocol) servers that expose Systemonomic's Work Domain Analysis, ATSS assessment, and organizational design capabilities to AI agents (Claude Desktop, Cursor, Claude Code, etc.).
Power your agent
The publisher's own command. Not a PlugMCP endpoint.
Host a copy with our keys and logs.
uvx systemonomic-mcp==0.1.1Claude Code has no install link for a local command. Copy the command below and run it in a terminal.
SYSTEMONOMIC_API_KEY=<secret> claude mcp add io.github.tonyc23-systemonomic-mcp -- uvx systemonomic-mcp==0.1.1Run in a terminal.
{ "mcpServers": { "io.github.tonyc23-systemonomic-mcp": { "command": "uvx", "args": [ "systemonomic-mcp==0.1.1" ], "env": { "SYSTEMONOMIC_API_KEY": "<secret>" } } }}claude_desktop_config.json
Remote URL or a command on your machine? How the two installs differ
Run on PlugMCP
A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.
LaunchFrom the publisher
MCP (Model Context Protocol) servers that expose Systemonomic's Work Domain Analysis, ATSS assessment, and organizational design capabilities to AI agents (Claude Desktop, Cursor, Claude Code, etc.).
Model work domains, score tasks for AI suitability, and design organisations.
pip install systemonomic-mcp
sk_sys_) — it's shown only onceSet the environment variable:
export SYSTEMONOMIC_API_KEY="sk_sys_your_key_here"
Optionally, point to a different API endpoint (defaults to production):
export SYSTEMONOMIC_API_URL="https://your-dev-backend.up.railway.app"
Add to your claude_desktop_config.json:
{
"mcpServers": {
"systemonomic-wda": {
"command": "python",
"args": ["-m", "systemonomic_mcp.wda_server"],
"env": {
"SYSTEMONOMIC_API_KEY": "sk_sys_your_key_here"
}
},
"systemonomic-atss": {
"command": "python",
"args": ["-m", "systemonomic_mcp.atss_server"],
"env": {
"SYSTEMONOMIC_API_KEY": "sk_sys_your_key_here"
}
},
"systemonomic-org": {
"command": "python",
"args": ["-m", "systemonomic_mcp.org_server"],
"env": {
"SYSTEMONOMIC_API_KEY": "sk_sys_your_key_here"
}
}
}
}
In Cursor Settings → MCP Servers, add each server:
systemonomic-wdapython -m systemonomic_mcp.wda_serverSYSTEMONOMIC_API_KEY=sk_sys_...Repeat for atss_server and org_server.
systemonomic-wda — Work Domain Analysissystemonomic-atss — Assessment & Taskssystemonomic-org — Organizational DesignYou: Generate a WDA for our university procurement department. They handle purchase requests, vendor management, contract negotiation, and compliance with government regulations.
Claude: Uses
create_project→generate_wda→ returns the generated hierarchy
You: For the procurement project, derive tasks from the WDA and run an automation assessment.
Claude: Uses
generate_tasks_from_wda→run_atss_batch→ summarizes automation candidates
You: Create a PDF report for the procurement project.
Claude: Uses
generate_pdf_report→ saves the PDF
# Run a server locally for testing
cd mcp
pip install -e .
SYSTEMONOMIC_API_KEY=sk_sys_... python -m systemonomic_mcp.wda_server
# Use the MCP inspector
SYSTEMONOMIC_API_KEY=sk_sys_... mcp dev src/systemonomic_mcp/wda_server.py
© 2026 MCP Directory.
Model work domains, score tasks for AI suitability, and design organisations.
{ "mcpServers": { "io.github.tonyc23-systemonomic-mcp": { "command": "uvx", "args": [ "systemonomic-mcp==0.1.1" ], "env": { "SYSTEMONOMIC_API_KEY": "<secret>" } } }}Tools
No tool list yet. Remote servers get one after the first health check.
FAQ
Copy the stdio config for Cursor, Claude Desktop, Claude Code, or VS Code on this page. It uses the publisher's install, not a PlugMCP proxy.
The publisher lists these environment variables: SYSTEMONOMIC_API_KEY.
Systemonomic — Work Domain Analysis & AI Task Suitability runs locally over stdio.
Last checked 2026-11-09. Model work domains, score tasks for AI suitability, and design organisations.