Capstone – The Sovereign Trading Terminal

Q: Why use MCP instead of standard LangChain Tools?

Portability. If you write a Postgres Tool in LangChain, it only works in LangChain. If you spin up a Postgres MCP Server, any MCP-compliant client (Claude Desktop, Zed IDE, or your custom agent) can use it instantly. It is future-proofing your code.

Q: What hardware do I need to run this locally?

To run Llama 3 8B (quantized) comfortably alongside the orchestration logic, you need an NVIDIA GPU with at least 8GB VRAM (e.g., RTX 3060 or higher) or a Mac M-Series chip with 16GB+ Unified Memory.

Q: Can I swap Llama 3 for GPT-4 if I don't care about privacy?

Yes. Because the architecture uses the MCP standard, the Brain is decoupled from the Body (Tools). You can change the model endpoint in your config file, and the agents will still know how to use the SQLite and Brave tools without code changes.

Q: Is the Brave Search API free?

Brave offers a free tier (2,000 queries/month), which is sufficient for development and testing. For a production bot polling every minute, you would need a paid plan.

The Ultimate Integration Test
Author: AgileWoW Team
Category: Sovereign AI / Model Context Protocol
Read Time: 15 Minutes
Parent Guide: The Agentic AI Engineering Handbook

The era of "rented intelligence" is ending. For strictly private financial operations, relying on cloud-based APIs introduces latency, cost, and unacceptable privacy risks. You send your strategy out; you pray the server stays up.

The Sovereign Trading Terminal is a Tier 3 agentic system designed to run entirely on your local infrastructure. It represents the "Graduation Project" of this handbook.

It solves the "Spaghetti Code" problem. Instead of writing custom wrappers for every database and API, this system utilizes the Model Context Protocol (MCP)—the new "USB-C" standard for AI—to create a universal bus where agents plug into tools seamlessly.

1. The Design Challenge: The Ultimate Integration Test

Building a single chatbot is trivial. Orchestrating a swarm of agents that can research, analyze, and "trade" (simulate) without hallucinating—while running offline—is a different beast.

The Complexity Matrix:

The Agents: We need 4 distinct personas (Analyst, Risk Manager, Execution Bot, Sentiment Scout).
The Infrastructure: They must share memory and tools without crashing.
The Protocol: They must communicate via MCP Servers, not hardcoded API strings.
The Goal: A "Zero-Leakage" environment. Your trade history, your strategy, and your PnL never leave your localhost.

2. The Tech Stack Selection

To build a system that is both sovereign and capable, we rely on the bleeding edge of the local AI ecosystem.

Component	Choice	Why?
Protocol	Anthropic MCP SDK	The new industry standard for connecting LLMs to context and tools.
Orchestration	LangGraph	We need a state machine to manage the "Proposal -> Validation -> Execution" workflow.
Intelligence	Llama 3 (via Ollama)	True Sovereignty. Run the brain locally on your GPU.
Database	SQLite MCP	Zero-latency, serverless local storage for price history and logs.
Search	Brave Search MCP	Privacy-preserving web search for the "Sentiment Scout" (no tracking).

3. Architecture Deep Dive: The MCP Server Mesh

3.1 The 6 Distinct MCP Servers

The core innovation is decoupling. Our agents don't have hardcoded tools; they have access to an MCP Server Mesh.

File System MCP: Gives agents direct read/write access to local trade logs (/logs/trades.json) and strategy configs.
SQLite MCP: A persistent local database for historical price data and portfolio state.
Brave Search MCP: Allows the "Sentiment Scout" to browse the live web for news.
TimeAPI MCP: A simple but critical server. LLMs are frozen in training time; this server grounds them in the "Now."
Fetch MCP: A generic HTTP client for fetching raw JSON from crypto/stock APIs (e.g., CoinGecko).
Memory MCP: A knowledge graph where agents leave "sticky notes" for one another.

3.2 The Agent Workflow ("The Relay Race")

How does a trade happen in a sovereign system?

Sentiment Scout (Agent 1): Wakes up via TimeAPI, uses Brave Search to find breaking news, and logs it to the File System.
Market Analyst (Agent 2): Reads the file, queries SQLite for price history, and proposes a trade.
Risk Manager (Agent 3): Intercepts the proposal. It checks the portfolio balance in SQLite.
- If risk > 2%: REJECT.
- If risk < 2%: APPROVE.
Execution Bot (Agent 4): Formats the order and saves the "Pending Execution" state to the log.

4. Implementation Guide (MCP SDK)

Phase 1: The MCP Server Config

You define your servers in a simple JSON configuration file that the MCP SDK reads.

{
  "mcpServers": {
    "sqlite": {
      "command": "uvx",
      "args": ["mcp-server-sqlite", "--db-path", "./data/trading.db"]
    },
    "brave-search": {
      "command": "uvx",
      "args": ["mcp-server-brave-search"],
      "env": { "BRAVE_API_KEY": "YOUR_KEY" }
    }
  }
}

Phase 2: The Agent "Handshake"

Using the SDK, your Python agent connects to these servers instantly.

from mcp import ClientSession
# Connect to the SQLite Server
async with ClientSession(server_params) as session:
    # The agent can now "see" the database tools automatically
    tools = await session.list_tools()

    # Agent asks to run a query (No SQL injection risk if using parameterized queries)
    result = await session.call_tool("query_db", {"sql": "SELECT price FROM btc_history LIMIT 5"})

Phase 3: The "Circuit Breaker"

We implement a Human-in-the-Loop check. The Execution Bot cannot "Write" to the real broker API; it can only write to a pending_orders.json file. A human must manually approve the file execution script to prevent "Flash Crash" scenarios.

5. Use Cases for Sovereign AI

Crypto Market Maker: Run a grid-trading bot that adjusts its range based on local volatility analysis without exposing your algorithm to cloud logs.

Private Wealth Dashboard: A "Talk to your Portfolio" interface where you can ask, "How did my tech stocks perform compared to inflation?" without uploading your bank statements to ChatGPT.

Regulatory Compliance: For institutions that legally cannot send client data to OpenAI, this architecture provides an on-premise, compliant alternative.

6. Frequently Asked Questions (FAQ)

Q1: Why use MCP instead of standard LangChain Tools?

A: Portability. If you write a "Postgres Tool" in LangChain, it only works in LangChain. If you spin up a "Postgres MCP Server," any MCP-compliant client (Claude Desktop, Zed IDE, or your custom agent) can use it instantly. It is future-proofing your code.

Q2: What hardware do I need to run this locally?

A: To run Llama 3 8B (quantized) comfortably alongside the orchestration logic, you need an NVIDIA GPU with at least 8GB VRAM (e.g., RTX 3060 or higher) or a Mac M-Series chip with 16GB+ Unified Memory.

Q3: Can I swap Llama 3 for GPT-4 if I don't care about privacy?

A: Yes. Because the architecture uses the MCP standard, the "Brain" is decoupled from the "Body" (Tools). You can change the model endpoint in your config file, and the agents will still know how to use the SQLite and Brave tools without code changes.

Q4: Is the Brave Search API free?

A: Brave offers a free tier (2,000 queries/month), which is sufficient for development and testing. For a production bot polling every minute, you would need a paid plan.

7. Sources & References

Architecture & Frameworks

Anthropic: Model Context Protocol (MCP) Documentation – The official standard for connecting AI models to systems.
LangGraph: Multi-Agent Workflows – Guide on building stateful agent teams.

Tools & Infrastructure

Ollama: Get Up and Running with Llama 3 – The easiest way to run local LLMs.
Brave: Brave Search API for AI – Privacy-preserving search index.
SQLite: MCP Server Implementation – The reference implementation for database connectivity.