Systemonomic — Work Domain Analysis & AI Task Suitability

io.github.TonyC23Published by GitHub account TonyC23v0.1.2Updated Oct 24, 2026

Model work domains, score tasks for AI suitability, and design organisations.

VerifiedSTDIODesktop onlyProductivity & WorkflowData & AnalyticsBusiness & Commerce

Overview

AI-generated overview

A local MCP server that lets an assistant model work domains, score tasks for AI suitability, and design organizational structures through the Systemonomic API.

What you can do
Create, inspect, update, delete and export/import work domain analysis projects, nodes and means-ends links, including AI-generated WDAs from a text description. Generate, list and accept tasks and AI task suggestions, then run ATSS assessments and review stored results and run history. Read and save organizational designs, propose and apply restructures, validate RACI matrices, manage version snapshots, and generate PDF reports.
When to use it
Useful when mapping a department's or process's work domain, deciding which tasks are suitable for automation or AI, or drafting and validating organizational structures and RACI matrices with an assistant.
Requirements
Runs locally as a Python package installed with pip, launched as separate wda, atss and org server modules. Requires a Systemonomic account and an API key supplied in the SYSTEMONOMIC_API_KEY environment variable; network access to the Systemonomic API is needed, and SYSTEMONOMIC_API_URL can point to a different endpoint.
Before you install
The API key is a secret shown only once at generation and must be kept out of shared config files. Many tools write, change or delete data in your Systemonomic account, including deleting nodes and links, applying restructure proposals, and persisting assessments and org designs. Project content is sent to the Systemonomic API for AI generation and assessment.

Installation

In SourceWeft

  1. Open Systemonomic — Work Domain Analysis & AI Task Suitability in the dashboard and add it to a workspace.
  2. Enable the server for the chats that should use its tools.

Desktop only via STDIO. STDIO servers start a local process, so they need the SourceWeft desktop host.

Other MCP clients

Follow the launch instructions in the repository.

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

bash
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:

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

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

bash
# Run a server locally for testingcd mcppip install -e .SYSTEMONOMIC_API_KEY=sk_sys_... python -m systemonomic_mcp.wda_server
# Use the MCP inspectorSYSTEMONOMIC_API_KEY=sk_sys_... mcp dev src/systemonomic_mcp/wda_server.py

Source: README.md at commit a307a7c

Tools

0
SourceWeft has not started this package in its sandbox yet, so its tools are not known.

Version history

1
  1. v0.1.2LatestSep 16, 2026

Score

40% of the applicable checks met

How complete this listing is, computed from the registry entry, the endpoint and public sources. Not a rating of the server and not a statement that it is safe or working today: reachability is a dated check, and no installs or reviews go into the score.

  • Reached in the last 30 days25 pts

    Keep the endpoint answering the MCP handshake; SourceWeft probes it every 30 days.

  • Auth declared10 pts

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  • Tools listed15 pts

    Answer tools/list without a credential, or ship a package that starts in a sandbox without one.

  • DescriptionRequired15 pts
  • Logo10 pts
  • Documentation15 pts
  • License10 pts

    Declare a license in the repository or the registry entry.