Brand Knowledge Server

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.

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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.

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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

Decision	Choice	Reason
MCP server framework	Raw mcp SDK	FastMCP's stdout banner corrupts JSON-RPC stream when spawned as subprocess
Transport	stdio	HTTP introduced connection errors in prior builds; stdio is stable for subprocess spawning
Orchestration	LangGraph StateGraph	Explicit nodes, conditional edges, loop-backs; CrewAI's MCPServerAdapter exposes Tools only
Model	qwen2.5 (Ollama)	llama3.2 sends typed tool arguments as strings, breaking schema validation — closed finding
Python version	3.11	Python 3.14 incompatible with anyio subprocess stdio handling

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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

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Mock Data

Two Toyota dealers, intentionally differentiated to force meaningful routing:

	Toyota Metro Manila	Toyota Cebu South
dealer_id	toyota-metro-manila-01	toyota-cebu-south-02
Location	Quezon City, NCR	Cebu City, Visayas
Inventory	5 models	4 models
Promotions	Financing-focused	Process-focused
Hours	Mon–Sat	Mon–Sun incl. holidays
Avg. review rating	4.4	4.2

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

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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

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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
...

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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

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Dependencies

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

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Known Issues

Issue	Status	Notes
Python 3.14 incompatible	Confirmed	anyio 4.14.0 TaskGroup subprocess stdio fails on Python 3.14. Use Python 3.11.
FastMCP stdout corrupts stdio	Confirmed	FastMCP 3.x banner prints to stdout, breaking JSON-RPC handshake. Raw mcp SDK used instead.
create_react_agent deprecation warning	Cosmetic	Deprecated in LangGraph V1.0, not removed until V2.0. Resolved by upgrading to real StateGraph.
Git Bash heredoc syntax	Confirmed	cat > file << 'EOF' writes the command itself into the file on Git Bash/Windows. Use VS Code to create files instead.

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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.

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Documentation

File	Description
brand-knowledge-agent-architecture.md	Architecture document — planning decisions, build sequence, bug log, test results
brand-knowledge-agent-project-doc.md	Full project documentation from problem statement to implementation
mcp-langgraph-vs-crewai-comparison.md	Comparison with the June 11 CrewAI + MCP Shopping Assistant project

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Author

Julianna Lacaden — CS Intern, June 2026