horizon-mcp-demo-extended

Extends the horizon-mcp-demo with a second MCP server wrapping the Horizon Data Predictive Model, demonstrating the Claudeception pattern — a single Claude agent connecting to two governed data systems simultaneously.

Built as research and reference 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 demonstrates

The core pattern: Two governed data systems, one agent, reasoning across both simultaneously.

The agent connects to both servers simultaneously, discovers what each one exposes, and reasons about how to bridge them — including explicitly surfacing the schema mismatch between the two systems and the governance risks of any mapping it proposes.

Related MCP server: Agency AI MCP Server

The schema mapping problem (Option C)

The two systems use completely different schemas:

Insurance data: product_type (auto/homeowners), state (TX/CA/FL/NY)

Predictive model: ServiceLine (Audit/Tax/Advisory), ClientType (Business/Individual), Industry (Healthcare/Financial Services/Technology/Manufacturing), NewVsExisting (Existing/New), LeadSource (Referral/Competitive)

These fields do not map cleanly to each other. The demo provides a pre-specified mapping for some questions, but Question 3 deliberately asks the agent to reason about the mapping problem before applying it — surfacing the governance risks of cross-system field mapping in a way that documents rather than hides the assumption.

This is the real-world use case: someone has a predictive model and a data source with different schemas, and needs an agent to connect them intelligently.

Project structure

horizon-mcp-demo-extended/
├── data/
│   └── seed_predictive_model.py    # Generates predictive_model.duckdb from scratch
├── mcp_server/
│   └── predictive_model_mcp_server.py  # Model-side MCP server (4 tools)
├── scripts/
│   └── run_two_server_agent.py     # Claudeception demo — two servers, one agent
├── logs/                           # Created at runtime — token logs per run
├── docs/                           # Additional documentation
├── requirements.txt
├── .gitignore
└── README.md

Prerequisites

• Python 3.12

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

  • The two-server agent looks for the insurance MCP server at ../horizon-mcp-demo/mcp_server/horizon_mcp_server.py

• 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

This generates data/predictive_model.duckdb — a DuckDB replica of the SQL Server model's trained segment estimates. Takes about 30 seconds.

5. Run the two-server demo

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

Demo questions

Predictive model input contract

Predictive model outputs

Notes

• The predictive model database (data/predictive_model.duckdb) is excluded from version control — generated locally by the seed script.

• The predictive model tools run as direct Python function calls — this avoids a Windows asyncio/anyio compatibility issue with nested stdio MCP clients while preserving the same agent behavior.

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

• The Claudeception pattern works with any MCP-compatible agent, not just Claude — the two servers are independent and do not communicate with each other directly.