Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@horizon-predictive-model-mcp Score a new audit opportunity for a manufacturing company."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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.
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:
Domain compatibility check — do the analytical categories in System A correspond to anything meaningful in System B?
Approved mapping — who has authority to approve the field-level mapping, under what conditions, and what triggers a review?
Governed interface definition — what kinds of upstream data sources are valid inputs semantically, not just technically?
Related MCP server: Agency AI MCP Server
System | Transport | What it exposes |
Insurance Data (from | MCP via stdio | P&C insurance semantic layer — loss ratio, claim frequency, earned premium by segment |
Predictive Model (this repo) | Direct Python calls | Professional services pipeline model — win probability, fees, margin, milestone timing |
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.
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.mdPython 3.12
horizon-mcp-demo cloned at the same directory level as this repo
horizon-mcp-demo fully built (dbt seed && dbt run completed)
git clone https://github.com/christianashworth/horizon-mcp-demo-extended.git
cd horizon-mcp-demo-extendedpy -3.12 -m venv .venv
.venv\Scripts\activatepython -m pip install --upgrade pip
pip install -r requirements.txtpython data/seed_predictive_model.py$env:ANTHROPIC_API_KEY = "your-api-key-here"
python scripts/run_two_server_agent.py# | Question | Purpose |
1 | What inputs does the predictive model require? | Establish model requirements |
2 | What are the loss ratios by segment in the insurance data? | Governed single-system query |
3 | Map insurance segments to model inputs and score | Surfaces the schema mismatch and governance failure |
4 | Score all homeowners state segments and combine with loss ratios | Cross-system join — analytically meaningless given the incompatible schemas |
5 | What governance decisions would be needed before using this mapping in production? | Agent correctly identifies what should have been done first |
Question 5 is the most instructive: the agent accurately describes the governance framework that was absent throughout the rest of the demo.
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.
This server cannot be deployed
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