Systemonomic
# Systemonomic MCP Servers
<!-- mcp-name: io.github.TonyC23/systemonomic-mcp -->
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.).
## Quick Start
### 1. Install
```bash
pip install systemonomic-mcp
```
### 2. Get an API Key
1. Log in to [Systemonomic](https://systemonomic.com)
2. Go to **Profile** → **API Keys**
3. Click **Generate API Key**
4. Copy the key (starts with `sk_sys_`) — it's shown only once
### 3. Configure
Set the environment variable:
```bash
export SYSTEMONOMIC_API_KEY="sk_sys_your_key_here"
```
Optionally, point to a different API endpoint (defaults to production):
```bash
export SYSTEMONOMIC_API_URL="https://your-dev-backend.up.railway.app"
```
### 4. Add to Claude Desktop
Add to your `claude_desktop_config.json`:
```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"
}
}
}
}
```
### 5. Add to Cursor
In Cursor Settings → MCP Servers, add each server:
- **Name:** `systemonomic-wda`
- **Command:** `python -m systemonomic_mcp.wda_server`
- **Environment:** `SYSTEMONOMIC_API_KEY=sk_sys_...`
Repeat for `atss_server` and `org_server`.
## Available Servers
### `systemonomic-wda` — Work Domain Analysis
| Tool | Description |
|------|-------------|
| `list_projects` | List all projects |
| `get_project_state` | Get complete project state |
| `create_project` | Create a new project |
| `get_project_stats` | Get project statistics |
| `list_wda_nodes` | List WDA nodes |
| `create_wda_node` | Create a node at a WDA level |
| `update_wda_node` | Update a node's label/level/description |
| `delete_wda_node` | Delete a node |
| `list_wda_links` | List means-ends links |
| `create_wda_link` | Link two nodes |
| `delete_wda_link` | Remove a link |
| `generate_wda` | AI-generate a full WDA from a text description |
| `export_project` | Export project as JSON |
| `import_wda` | Import nodes and links |
### `systemonomic-atss` — Assessment & Tasks
| Tool | Description |
|------|-------------|
| `list_tasks` | List project tasks |
| `create_task` | Create a task |
| `generate_tasks_from_wda` | Auto-generate tasks from WDA Objects |
| `derive_task_suggestions` | AI-derived task suggestions |
| `list_suggestions` | List pending suggestions |
| `accept_suggestions` | Promote suggestions to tasks |
| `run_atss_batch` | Run ATSS assessment on all tasks |
| `get_atss_results` | Get stored assessment results |
| `persist_atss_results` | Save assessment results |
| `list_atss_runs` | List assessment run history |
### `systemonomic-org` — Organizational Design
| Tool | Description |
|------|-------------|
| `get_org_design` | Get current roles, org units, allocations |
| `persist_org_design` | Save org design |
| `propose_restructure` | AI-generated restructure proposal |
| `apply_proposal` | Apply a restructure proposal |
| `validate_raci` | Validate RACI matrix |
| `create_org_snapshot` | Create version snapshot |
| `list_org_snapshots` | List snapshots |
| `export_org_design_json` | Export as JSON |
| `generate_pdf_report` | Generate comprehensive PDF report |
| `get_report_status` | Check report readiness |
## Example Conversations
### "Help me model our procurement process"
> **You:** 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*
### "Assess which tasks can be automated"
> **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*
### "Generate the full report"
> **You:** Create a PDF report for the procurement project.
>
> **Claude:** *Uses `generate_pdf_report` → saves the PDF*
## Development
```bash
# 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
```
TDQS
Scored across 10 tools
Most tools have distinct purposes, but there is some potential confusion between 'derive_task_suggestions' and 'generate_tasks_from_wda' as both generate tasks from WDA objects, with the former being described as 'more sophisticated.' The other tools are clearly differentiated, covering suggestion management, task creation, ATSS assessment, and listing operations.
All tool names follow a consistent snake_case pattern with clear verb_noun structures (e.g., 'accept_suggestions', 'create_task', 'list_tasks'). This uniformity makes the tool set predictable and easy to navigate, with no deviations in naming conventions.
With 10 tools, the server is well-scoped for managing tasks, suggestions, and ATSS assessments in a project automation system. Each tool serves a specific role in the workflow, from creation and listing to analysis and persistence, without feeling bloated or sparse.
The tool set covers core workflows for task and suggestion management, including creation, listing, and assessment, but lacks update and delete operations for tasks or suggestions. This minor gap might require workarounds, but the overall surface supports the domain of project automation and AI-driven task analysis effectively.