Horizon Predictive Model

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.

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What this demonstrates

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

System	Transport	What it exposes	Tools
Insurance Data (from horizon-mcp-demo)	MCP via stdio	P&C insurance semantic layer — loss ratio, claim frequency, earned premium by segment	list_models, get_model_details, get_metric_definitions, query_data
Predictive Model (this repo)	Direct Python calls	Professional services pipeline prediction model — win probability, fees, margin, milestone timing	describe_inputs, describe_outputs, validate_payload, score_opportunity

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.

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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.

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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

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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)

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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

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Demo questions

#	Question type	Servers used
1	Model understanding — what does the predictive model need?	Predictive Model only
2	Governed data query — insurance loss ratios by segment	Insurance Data only
3	Schema mapping surfaced — reason about mapping insurance fields to model inputs before scoring	Both
4	Cross-server analysis — score all homeowners segments and combine with loss ratio data	Both
5	Governance reflection — what decisions need to be made before using this mapping in production?	Both

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Predictive model input contract

Field	Type	Allowed values	Notes
ServiceLine	string	Audit, Tax, Advisory	Required. Highest priority — dropped last.
ClientType	string	Business, Individual	Required
Industry	string	Healthcare, Financial Services, Technology, Manufacturing	Optional (nullable for Individuals)
NewVsExisting	string	Existing, New	Required
LeadSource	string	Referral, Competitive	Required. Lowest priority — dropped first.

Predictive model outputs

Output	Description
WinPct	Probability of winning (0.0–1.0)
NetFees	Estimated net fees in USD if won
MarginPct	Estimated margin percentage
DaysSellToStart	Estimated days from contract signing to work start
DaysStartTo50Pct	Estimated days from work start to 50% completion
DaysStartTo100Pct	Estimated days from work start to 100% completion

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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.