High Gear Collision MCP Server
Click on "Install 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., "@High Gear Collision MCP ServerCheck State Farm's labor rate and parts policy for a 2021 Civic bumper, then draft an estimate."
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.
High Gear Collision MCP Server
An MCP server for High Gear Collision (Mico, TX) that gives an AI assistant direct access to insurer labor rates, parts approval policies, and claim status — so an estimator can draft and reconcile a repair estimate against what an insurer will actually approve, instead of a phone/email back-and-forth.
This is a working prototype: the "insurer data" in shop_data.py is mocked
in-memory so you can run and test the whole flow without real insurer
credentials. Swap shop_data.py's internals for real API calls or a
shop-maintained database when you're ready to go live.
Project structure
high-gear-mcp/
├── shop_data.py # Business logic + mock insurer data (no MCP dependency)
├── server.py # MCP tool definitions, wraps shop_data.py
├── test_client.py # Standalone MCP client — talks to server.py over the real protocol
├── requirements.txt
└── README.mdBusiness logic lives separately from the MCP wiring on purpose — you can
test and evolve shop_data.py (e.g. point it at a real insurer API) without
touching the tool definitions in server.py.
Related MCP server: mcp-ohmy-sql
Setup
python3 -m venv venv
source venv/bin/activate # on Windows: venv\Scripts\activate
pip install -r requirements.txtTest the logic directly (no MCP client needed)
python3 -c "
import shop_data as s
print(s.get_insurer_labor_rates('State Farm'))
print(s.get_parts_approval_policy('state_farm', part='bumper'))
"Run with the MCP Inspector (interactive testing)
mcp dev server.pyThis opens a local web UI where you can call each tool by hand and see exactly what gets sent/returned — the fastest way to sanity-check before wiring it into Claude.
Run the standalone test client (full protocol round-trip)
python3 test_client.pyThis is different from calling shop_data.py's functions directly — it
spawns server.py as a real subprocess, connects over stdio, does the
MCP initialize handshake, calls list_tools to discover what the
server advertises, then calls all 5 tools (including a deliberate
error case and a full estimate → supplement → status flow) exactly the
way Claude Desktop would. Good for proving the server works end-to-end
before you ever plug it into Claude, and a good artifact to show in an
interview: it demonstrates the difference between "the logic works" and
"the protocol contract works."
Connect to Claude Desktop
Add this to your claude_desktop_config.json (Claude menu → Settings →
Developer → Edit Config on Mac; %APPDATA%\Claude\claude_desktop_config.json
on Windows):
{
"mcpServers": {
"high-gear-collision": {
"command": "python3",
"args": ["/absolute/path/to/high-gear-mcp/server.py"]
}
}
}Restart Claude Desktop. You should see "High Gear Collision" appear under your MCP tools, with all 5 tools available.
The 5 tools
Tool | Type | Purpose |
| Read | Approved hourly rate by insurer + repair category |
| Read | OEM/aftermarket policy by insurer, with part-level exceptions |
| Read | Current approval status + adjuster notes for a claim |
| Write | Submit a drafted estimate for insurer approval |
| Write | Submit a supplemental request on an existing claim |
Try it out
Once connected, ask Claude something like:
"I've got a 2021 Honda Civic in for a front bumper repair with State Farm — 3.5 hours of body work and a bumper that costs $240. Check their labor rate and parts policy for a bumper, then draft the estimate."
Claude will call get_insurer_labor_rates, get_parts_approval_policy, do
the math, and — after you confirm — call submit_estimate.
Known limitations (worth naming if this comes up in an interview)
Mock data:
shop_data.py's insurer rates/policies are hardcoded, not pulled from real insurer systems. Real insurers don't generally expose clean APIs for this — the realistic path is a shop-maintained internal database, kept in sync on a defined refresh cadence (see thelast_verifiedfield returned by every read tool).No persistence: claims submitted via
submit_estimatelive only in memory and reset when the server restarts. A real deployment needs a proper database.No real auth: this prototype has no authentication layer at all. A production version needs per-insurer credentials and role-based access so an estimator can only act on claims for their own shop.
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