horizon-mcp-demo-extended

Extends the horizon-mcp-demo by connecting it to the Horizon Data Predictive Model — a professional services pipeline model — via a single Claude agent. Built as research material for the Horizon Data Partners white paper: The Governed Data Layer: Why AI Agents Fail Without One, and How to Build It.

What this actually demonstrates

This demo was built to explore the Claudeception pattern — a single agent connecting to two governed systems and reasoning across them. In practice, it produced something more instructive than a clean success: a concrete example of what happens when cross-system governance is absent.

The two systems in this demo have completely incompatible analytical schemas:

• Insurance data — P&C insurance policies with product types (auto/homeowners) and states (TX/CA/FL/NY)

• Predictive model — professional services pipeline model expecting ServiceLine (Audit/Tax/Advisory), ClientType (Business/Individual), Industry, NewVsExisting, and LeadSource

These categories do not correspond to anything real across the two systems. P&C insurance policies are not professional services opportunities. Yet the agent — when asked to map insurance segments to model inputs — did so confidently. It reasoned about the mapping, cited governance risks, validated its payload, and returned a WinPct estimate that was internally consistent and analytically meaningless.

This is the paper's argument made concrete: a governed semantic layer protects an agent within a system. It cannot protect against an agent being pointed at two systems that have no valid analytical relationship. The agent followed its governance rules correctly. It produced nonsense anyway — because cross-system governance was never done.

Three things were missing that cross-system governance would have required:

1. Domain compatibility check — do the analytical categories in System A correspond to anything meaningful in System B?

2. Approved mapping — who has authority to approve the field-level mapping, under what conditions, and what triggers a review?

3. Governed interface definition — what kinds of upstream data sources are valid inputs semantically, not just technically?

Related MCP server: Agency AI MCP Server

Technical notes

The predictive model tools run as direct Python function calls rather than via a second MCP server. This resolves a Windows asyncio/anyio compatibility issue with nested stdio clients. On Linux/macOS, the full two-MCP-server pattern works correctly.

The predictive model's trained segment estimates are loaded from a DuckDB file into memory at startup, generated by data/seed_predictive_model.py from synthetic training data using the same parameters as the original SQL Server model.

Project structure

horizon-mcp-demo-extended/
├── data/
│   └── seed_predictive_model.py    # Generates predictive_model.duckdb
├── mcp_server/
│   └── predictive_model_mcp_server.py  # MCP server definition (Linux/macOS)
├── scripts/
│   └── run_two_server_agent.py     # Agent demo — insurance MCP + model Python calls
├── requirements.txt
├── .gitignore
└── README.md

Prerequisites

• Python 3.12

• horizon-mcp-demo cloned at the same directory level as this repo

• horizon-mcp-demo fully built (dbt seed && dbt run completed)

Setup (Windows)

1. Clone this repo

git clone https://github.com/christianashworth/horizon-mcp-demo-extended.git
cd horizon-mcp-demo-extended

2. Create and activate virtual environment

py -3.12 -m venv .venv
.venv\Scripts\activate

3. Install dependencies

python -m pip install --upgrade pip
pip install -r requirements.txt

4. Generate the predictive model database

python data/seed_predictive_model.py

5. Run the demo

$env:ANTHROPIC_API_KEY = "your-api-key-here"
python scripts/run_two_server_agent.py

Demo questions

Question 5 is the most instructive: the agent accurately describes the governance framework that was absent throughout the rest of the demo.

Notes

• data/predictive_model.duckdb is excluded from version control — generated locally by the seed script.

• Token usage is logged per question in logs/two_server_run_<timestamp>.json.

• The full technical documentation is in horizon-mcp-demo-extended-technical-documentation.docx.