MO§ES™ Enterprise — AI Operator Evaluation Platform

Live: mos2es.org — Systems intelligence for the human-AI operating system. The new standard in performative metrics and benchmarks for AI operators. Baselines system intelligence for everyday operations and AI workflows. Content-free token telemetry. Never your prompts.

Enterprise AI operator evaluation. Measure how people operate AI — not the AI model itself.

MO§ES™ curates a company's system intelligence — how effectively people drive AI systems across tools, tasks, workflows, and conditions. The same way BI sees the business, MO§ES™ sees how the business operates AI.

Table of Contents

• What is MO§ES™?

• The MO§ES™ ecosystem

• Live surfaces

• Quick start

• The 11-step demo pipeline

• The 5 canonical metrics

• Repository structure

• MCP server

• Cloudflare Workers

• Key conventions

• Complement, don't replace

• License

Related MCP server: scan-your-ai-toolkit

What is MO§ES™?

MO§ES™ is an enterprise AI operator evaluation platform. It measures how people operate AI systems — not the AI models themselves, not usage volume, not self-reported proficiency. Using content-free token telemetry (input, output, cache read, cache write — no prompt text, no response text), MO§ES™ baselines system intelligence across:

• Operators — how effectively individuals drive AI

• Teams — cohort distributions and capability topology

• Workflows — where AI fits in the work, not just whether it's used

• Organizations — cross-team benchmarking and capability mapping

The platform builds bespoke evals around your workflows, roles, and AI systems, benchmarks performance against internal and external reference populations, diagnoses capability gaps and divergence patterns, tests targeted interventions, and re-measures what changes.

The positioning

BI sees the business. MO§ES™ sees how the business operates AI.

LMSYS benchmarks models. Braintrust evaluates product outputs. Langfuse traces LLM calls. WakaTime tracks time. CostHawk tracks spend. None of them see the system intelligence of everyday operations and AI workflows. MO§ES™ is the missing layer.

The MO§ES™ ecosystem

Live surfaces

Quick start

Run the demo (one-liner, no clone needed)

curl -sL https://mos2es.org/demo/run.py | python3 -

This clones the repo to a temp directory, installs rich, and runs the full 11-step demo pipeline. Requires Python 3.10+ and git.

Clone and run

git clone https://github.com/SunrisesIllneverSee/Moses_Enterprise_B2BPilot_.git
cd Moses_Enterprise_B2BPilot_/_01_platform
pip install rich
python3 -m src.cli.main demo full

Run the test suite

cd _01_platform
python3 -m pytest tests/ -q
# 527 tests pass

Run the MCP server locally

cd _01_platform
pip install mcp
python3 -m src.mcp_server.server

Use the remote MCP server

Add to your Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json on macOS):

{
  "mcpServers": {
    "moses": {
      "url": "https://mcp.mos2es.org/mcp",
      "transport": "http"
    }
  }
}

Or call tools directly:

curl -s -D /tmp/h -X POST https://mcp.mos2es.org/mcp \
  -H "Content-Type: application/json" \
  -d '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{}}' > /dev/null
SID=$(grep -i "^mcp-session-id:" /tmp/h | tr -d '\r\n' | sed 's/.*: //')
curl -s -X POST https://mcp.mos2es.org/mcp \
  -H "Content-Type: application/json" -H "MCP-Session-Id: $SID" \
  -d '{"jsonrpc":"2.0","id":2,"method":"tools/list"}' | python3 -m json.tool

The 11-step demo pipeline

The demo runs on fully synthetic data. No real operators, no real prompt text, no real API calls. Deterministic — running twice produces identical results.

The 5 canonical metrics

15 evaluation families. 13 benchmark classes. 4 measurement levels (Operator, Team, Workflow, Organization).

Repository structure

_01_platform/          Python platform (the product)
  src/                 Source code (domain, metrics, analysis, diagnostics, CLI, MCP server)
  tests/               527 tests
  demo_data/           Synthetic demo cohort (50 operators, 1,668 observations)
  scripts/             Utility scripts
  schemas/             JSON schemas

_02_demo-website/      Enterprise demo site (enterprise.mos2es.org)
_03_promo-site/        Promo site (mos2es.org)
  vs/                  16 competitor comparison pages
  alternatives/        8 alternatives listicle pages
  concepts/            10 concept explainer pages
  guides/              4 how-to guides
  demo/run.py          One-liner demo runner
  .well-known/mcp.json MCP server discovery

_04_onepager/          One-pager site

_workers/              Cloudflare Workers
  promo-worker/        mos2es.org worker (static assets + AEO/SEO/GEO headers)
  moses-worker/        enterprise.mos2es.org worker (static assets)
  onepager-worker/     One-pager worker (static assets)
  mcp-worker/          MCP server worker (computes live from raw data, 27 tools)

MCP server

The MCP server exposes 27 tools (22 read + 5 write) over streamable HTTP at https://mcp.mos2es.org/mcp. No authentication required for the public server.

Read tools include: cohort stats, operator profiles, metric distributions, benchmark positions, divergence findings, intervention outcomes, outcome lineage, workflow fit, org topology, and more.

Write tools include: create intervention, assign intervention, record outcome, create eval configuration, create pilot configuration.

The server computes live from raw observation data — no pre-computed results. Every call runs the actual scoring, benchmarking, and diagnostic engines.

Server card

curl -s https://mcp.mos2es.org/.well-known/mcp/server-card.json | python3 -m json.tool

Returns the full server card with all 27 tool schemas, transport info, and metadata.

Cloudflare Workers

Four Workers deploy from this repo:

Deploy

npm install -g wrangler

cd _workers/promo-worker && wrangler deploy
cd _workers/moses-worker && wrangler deploy
cd _workers/onepager-worker && wrangler deploy
cd _workers/mcp-worker && wrangler deploy

MCP worker data sync

The MCP worker computes live from raw data files (observations.js, lineages.js) auto-generated from the platform's demo data. To regenerate after updating demo data:

cd _workers/mcp-worker/src
python3 -c "
import json
with open('../../../_01_platform/demo_data/observations.jsonl') as f:
    data = [json.loads(l) for l in f if l.strip()]
with open('observations.js', 'w') as f:
    f.write('export default '); json.dump(data, f); f.write(';\n')
with open('../../../_01_platform/demo_data/lineages.jsonl') as f:
    data = [json.loads(l) for l in f if l.strip()]
with open('lineages.js', 'w') as f:
    f.write('export default '); json.dump(data, f); f.write(';\n')
"

Key conventions

• Brand: MO§ES™ (use the section sign §)

• All outcome claims are ASSOCIATION, never CAUSATION unless backed by a controlled experiment

• Composite score is DEVELOPMENTAL, never PERSONNEL — no punitive use, no employee leaderboard

• Operator similarity is metric similarity, NOT personality matching

• Content-free telemetry — no prompt text, no output bodies, no code content in observations

• No prompt-content surveillance — operator performance is observable from token structure alone

• Governance-ready — evidence labels (DEVELOPMENTAL, HYPOTHESIS, ASSOCIATION) on every output

Complement, don't replace

MO§ES™ works alongside your existing BI, eval suites, observability tools, and productivity analytics. It measures the systems intelligence layer they can't see. Not a replacement — the missing piece.

Five pillars

License

PolyForm Noncommercial 1.0.0 — see LICENSE. Personal use, research, and noncommercial use permitted. Commercial use requires a license. Patent pending.

mos2es.org · Book a demo · Run the demo · MCP server

Built by Deric J. McHenry — Ello Cello LLC