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Systemonomic — Work Domain Analysis & AI Task Suitability

Systemonomic — Work Domain Analysis & AI Task Suitability

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systemonomic.comBusiness & Marketing0Updated 29w ago

About Systemonomic — Work Domain Analysis & AI Task Suitability

Systemonomic provides work-domain analysis, AI task-suitability assessment, and organizational design for teams modeling work and planning structures. Its MCP servers cover workflows from generating domain models to assessing tasks and exporting organization reports.

Key features

  1. 01Create and edit work-domain nodes and links
  2. 02Generate a work-domain model from text
  3. 03Derive tasks and suggestions from models
  4. 04Assess task AI suitability in batches
  5. 05Propose and validate organizational changes
  6. 06Export project data and PDF reports

Use cases

  1. 01Map procurement or another department's work
  2. 02Derive tasks and identify automation candidates
  3. 03Validate a RACI matrix and plan restructuring
  4. 04Save organization snapshots and generate PDF reports

MCP support

It provides MCP servers for work-domain analysis, ATSS task assessment, and organizational design, exposing tools for projects, tasks, assessments, and organization design to AI agents.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

mcp.json · Universal config
{
"mcpServers": {
"tonyc23-systemonomic": {
"command": "uvx",
"args": [
"systemonomic-mcp"
],
"env": {
"SYSTEMONOMIC_API_KEY": "<SYSTEMONOMIC_API_KEY>"
}
}
}
}
Run commanduvx systemonomic-mcp
Environment variables to set
SYSTEMONOMIC_API_KEYSecretYour Systemonomic API key (generate at systemonomic.com/profile)

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
pypisystemonomic-mcp0.1.1

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

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.).

Quick Start

1. Install
pip install systemonomic-mcp
2. Get an API Key
  1. Log in to Systemonomic
  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:

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"
4. Add to Claude Desktop

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"
      }
    }
  }
}
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
ToolDescription
list_projectsList all projects
get_project_stateGet complete project state
create_projectCreate a new project
get_project_statsGet project statistics
list_wda_nodesList WDA nodes
create_wda_nodeCreate a node at a WDA level
update_wda_nodeUpdate a node's label/level/description
delete_wda_nodeDelete a node
list_wda_linksList means-ends links
create_wda_linkLink two nodes
delete_wda_linkRemove a link
generate_wdaAI-generate a full WDA from a text description
export_projectExport project as JSON
import_wdaImport nodes and links
systemonomic-atss — Assessment & Tasks
ToolDescription
list_tasksList project tasks
create_taskCreate a task
generate_tasks_from_wdaAuto-generate tasks from WDA Objects
derive_task_suggestionsAI-derived task suggestions
list_suggestionsList pending suggestions
accept_suggestionsPromote suggestions to tasks
run_atss_batchRun ATSS assessment on all tasks
get_atss_resultsGet stored assessment results
persist_atss_resultsSave assessment results
list_atss_runsList assessment run history
systemonomic-org — Organizational Design
ToolDescription
get_org_designGet current roles, org units, allocations
persist_org_designSave org design
propose_restructureAI-generated restructure proposal
apply_proposalApply a restructure proposal
validate_raciValidate RACI matrix
create_org_snapshotCreate version snapshot
list_org_snapshotsList snapshots
export_org_design_jsonExport as JSON
generate_pdf_reportGenerate comprehensive PDF report
get_report_statusCheck 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

# 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

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An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.