Jax Property MCP

Ask plain-English questions about Jacksonville property, flood risk and sales — answered by a local AI agent through tested MCP tools over PostGIS.

An MCP server and AI agent for Jacksonville (Duval County) property data, from 405k assessor records in PostGIS.

What it does

• search_properties: properties in a ZIP within a value range (optionally by home type)

• property_details: full assessor record for one parcel

• sale_history: recorded sales for one parcel

• recent_sales: recent sales in a ZIP, qualified (arm's-length) only by default

• zip_stats: count, median, average and max value for a ZIP

• flood_risk(strap): FEMA flood zones touching a parcel, whether each one is high-risk (SFHA), and what percent of the lot it covers. Data comes from FEMA's National Flood Hazard Layer, loaded by load_flood.py with completeness and geometry-repair checks (12,288 of 12,288 zones loaded, 0 lost).

Related MCP server: civic-library-mcp

Architecture

flowchart LR
  A1[Duval PA parcels / properties / addresses<br/>via jax-property-intelligence] --> DB[(PostgreSQL + PostGIS)]
  A2[FEMA NFHL<br/>load_flood.py] --> DB
  A3[FL DOR sales file<br/>load_sales.py] --> DB
  DB --> T[jax_dbt<br/>stg_addresses · stg_sales<br/>property_summary + tests]
  T --> DB
  DB --> S[server.py<br/>MCP tools · parameterized SQL]
  S <-->|stdio| AG[agent.py<br/>Ollama tool-calling loop]
  S <-->|stdio| VS[VS Code Copilot<br/>.vscode/mcp.json]
  AG --> EV[eval.py · hard_eval.py<br/>tool-choice evals]

AI / agent angle

agent.py connects to the server over stdio and runs a tool-calling loop with a local model on Ollama (default qwen3-coder:30b, override with AGENT_MODEL), at no API cost. The same server also works in VS Code Copilot through .vscode/mcp.json. eval.py and hard_eval.py check that each model picks the right tool — and declines out-of-scope questions.

Lesson learned: without zip_stats, the agent compared ZIPs by sampling 100 high-end listings and wrongly said 32210 was pricier than 32207. With the right tool, it used medians ($231k vs $184k) and got it right in half the time. Tool design matters more than model choice.

Quickstart (Windows)

Prerequisites: PostgreSQL + PostGIS with the parcel/property tables from jax-property-intelligence, and Ollama with a tool-calling model pulled. Clone to a local folder such as C:\dev\jax-property-mcp (not OneDrive).

cd C:\dev
git clone https://github.com/knightchrism/jax-property-mcp.git
cd jax-property-mcp
python -m venv .venv; .venv\Scripts\activate
pip install -r requirements.txt requests   # loaders also need requests
copy .env.example .env        # fill in DB_* (or set JAX_ENV_FILE to an existing .env)
python load_flood.py          # FEMA flood zones
python load_sales.py          # FL DOR sales
python clean_addresses.py     # logged address cleanup (addresses_qc)
python home_types.py          # DOR land-use → home type
python agent.py "Rank 32207, 32210 and 32205 by median value"

dbt models live in jax_dbt/ (dbt build runs the 13 data tests; needs a dbt-postgres profile).

Data Quality

Every loader keeps the raw data, builds a cleaned table, and logs exactly what changed, so no row is silently lost.

Every load is logged so changes to GIS and property data can be reviewed and audited. See DATA_SOURCES.md for sources, licensing, and disclaimers.

Data Pipeline (dbt)

Raw Duval County and Florida DOR data is cleaned in tested dbt staging models (stg_addresses, stg_sales) and joined into one row per parcel (property_summary, 405,722 parcels). 13 data tests run on every dbt build. The project lives in jax_dbt/.

Tech stack

Python · PostgreSQL / PostGIS · psycopg 3 · dbt · MCP Python SDK · Ollama · VS Code Copilot (MCP)

Disclaimer

Demo / testing only — not authoritative. Results are not a survey, title opinion, appraisal, official FEMA flood determination or legal determination, and data may be out of date. Verify with the Duval County Property Appraiser, FEMA and the Florida DOR before any financial, insurance or legal decision. Not affiliated with any of these agencies.

Credits

Data: FEMA National Flood Hazard Layer (public domain), Florida Department of Revenue Sales Data File, Duval County Property Appraiser / City of Jacksonville public records, Florida DOR land use codes — details in DATA_SOURCES.md. Software: PostgreSQL (PostgreSQL License), PostGIS (GPL-2.0), psycopg (LGPL-3.0), dbt-core (Apache-2.0), MCP Python SDK (MIT), Ollama Python client (MIT), python-dotenv (BSD-3-Clause), requests (Apache-2.0). Each keeps its own license.

License

Code: MIT, see LICENSE.