Brand Knowledge Agent

A standalone AI agent that exposes structured Toyota dealer brand data to an LLM via MCP (Model Context Protocol), grounding responses in verified dealer information rather than hallucinated or generic content.

Built during an internship exploration period at an AI product company focused on brand intelligence for LLM ingestion.

What It Does

When a user asks a question about a Toyota dealer — inventory, promotions, reviews, or profile — the agent:

1. Routes the query to the appropriate MCP tool

2. Retrieves structured data from the MCP server

3. Returns a grounded, accurate response using only what the tool returned

No hallucination. No generic filler. Only verified dealer data.

Related MCP server: Small Business MCP Server

Architecture

Mock Dealer Data (JSON)
        │
        ▼
┌───────────────────────────┐
│        MCP Server         │  raw mcp SDK, stdio transport
│  - get_inventory          │
│  - get_promotions         │
│  - get_reviews            │
│  - get_dealer_profile     │
│  - dealer://{id}/profile  │  MCP Resource
└─────────────┬─────────────┘
              │ stdio subprocess
              ▼
┌───────────────────────────┐
│    LangGraph StateGraph   │  qwen2.5 via Ollama
│  - call_model node        │
│  - tools node (ToolNode)  │
│  - conditional routing    │
│  - loop-back edge         │
└───────────────────────────┘

Why these choices

Project Structure

brand-knowledge-agent/
├── mcp_server/
│   ├── server.py                ← MCP server (4 tools + 1 resource)
│   └── mock_dealer_data.json    ← Two Toyota dealers (mock data)
├── agent/
│   └── agent.py                 ← LangGraph StateGraph agent
├── .gitignore
├── requirements.txt
└── README.md

Mock Data

Two Toyota dealers, intentionally differentiated to force meaningful routing:

This is mock data. Real dealer data integration is a Phase 2 dependency.

Setup

Requirements:

• Python 3.11 (3.14 is incompatible — see Known Issues)

• Ollama running locally with qwen2.5 pulled

Install Ollama model:

ollama pull qwen2.5

Clone and set up the project:

git clone https://github.com/LacadenJulianna/AI-Agents-with-MCP-and-LangGraph.git
cd AI-Agents-with-MCP-and-LangGraph

py -3.11 -m venv venv
source venv/Scripts/activate   # Git Bash
# or
venv\Scripts\activate          # cmd

pip install -r requirements.txt

Running the Agent

python agent/agent.py

The agent runs four test queries automatically and prints results to console. The MCP server is spawned as a subprocess — you do not run it separately.

Sample output:

=== Query: What vehicles does Toyota Metro Manila currently have in stock? ===

=== Agent Response ===
Toyota Metro Manila currently has the following vehicles in stock:
- Toyota Vios (1.3 XLE CVT, 2025): PHP 798,000 — 4 units in stock
- Toyota Fortuner (2.4 G Diesel 4x2 AT, 2025): PHP 1,950,000 — 2 units in stock
...

Testing the MCP Server (MCP Inspector)

To test individual tools without the agent, use MCP Inspector:

npx @modelcontextprotocol/inspector python mcp_server/server.py

Run this from cmd, not Git Bash — the Inspector's npx script does not resolve correctly in Git Bash on Windows.

Test cases to run:

• get_inventory with toyota-metro-manila-01 → 5 vehicles

• get_inventory with toyota-cebu-south-02 → 4 vehicles

• get_inventory with fake-dealer-99 → clean error with available IDs

• get_reviews with limit: 3 → 3 reviews with average rating

• Resources tab → List Templates → dealer://{dealer_id}/profile

Dependencies

mcp==1.28.0
fastmcp==2.3.3
langgraph
langchain-ollama
langchain-mcp-adapters==0.3.0

Known Issues

Phase 2 (Planned)

Phase 2 will add a monitoring agent that:

1. Sends dealer queries to multiple LLMs without the MCP knowledge feed

2. Compares responses against the MCP server as ground truth

3. Scores brand accuracy per LLM using a conditional edge and loop-back

This reuses the same MCP server (Phase 1 output = ground truth) and the same LangGraph conditional edge pattern. Blocked on real dealer data availability.

Documentation

Author

Julianna Lacaden — CS Intern, June 2026