Knowledge Base

📝 Context Summary

This guide provides a complete code walkthrough for creating a 100% local and private MCP client. It details how to use LlamaIndex to build an agent, serve a local LLM like Deepseek-R1 with Ollama, and connect to a simple SQLite MCP server. The workflow covers agent setup, tool discovery, and context-aware responses.

Building a 100% Local MCP Client

This guide provides a complete code walkthrough and explanation for building a fully local, private, and secure AI agent that uses the Model Context Protocol (MCP).

Introduction

An MCP client is a component within an AI application that establishes standardized connections to external tools and data sources via MCP. This implementation demonstrates how to build one that operates entirely on a local machine, ensuring data privacy and security.

Tech Stack

  • LlamaIndex: Used to build the MCP-powered Agent.
  • Ollama: Serves the local LLM (Deepseek-R1 in this example).
  • SQLite: A simple local database exposed via an MCP server.
  • LightningAI: For development and hosting (optional).

Workflow Summary

The operational flow is designed for privacy and local execution:

Workflow Diagram

  1. The user submits a query to the agent.
  2. The agent connects to the local MCP server to discover available tools.
  3. Based on the query, the agent invokes the correct tool (e.g., querying the SQLite database).
  4. The tool returns the necessary context to the agent.
  5. The agent uses the context to generate and return a final, context-aware response to the user.

Local MCP Client Implementation

1. Build an SQLite MCP Server

For this demonstration, a simple SQLite server is created with two tools: add_data and fetch_data. This keeps the example focused, but the client architecture can connect to any compliant MCP server.

SQLite MCP Server Tools

2. Set Up the Local LLM

We use Ollama to serve a local instance of the Deepseek-R1 model. This ensures that no data is sent to external APIs.

Ollama LLM Setup

3. Define the System Prompt

A clear system prompt instructs the agent to prioritize using its available tools to gather context before answering user queries.

System Prompt Definition

4. Define the LlamaIndex Agent

A function is created to build a LlamaIndex FunctionAgent. The tools discovered from the MCP server are passed to this agent, which LlamaIndex wraps as native, callable functions.

Agent Definition

5. Define Agent Interaction Logic

This component manages the conversation flow. It passes user messages to the FunctionAgent, maintains a shared context for memory, streams tool calls, and returns the final reply. All chat history and tool interactions are handled here.

Agent Interaction Logic

6. Initialize the MCP Client and Agent

Finally, the main script launches the MCP client, loads its tools, and wraps them for the LlamaIndex agent. The agent is then initialized with these tools and the context manager, making it ready for interaction.

Initialization

With these steps complete, the agent can be run locally to interact with the tools provided by the SQLite MCP server.

Key Concepts: local MCP client LlamaIndex agent Ollama Deepseek-R1 SQLite MCP server private AI

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About the Author: Adam Bernard

Building a 100% Local MCP Client with LlamaIndex and Ollama
Adam Bernard is a digital marketing strategist and SEO specialist building AI-powered business intelligence systems. He's the creator of the Strategic Intelligence Engine (SIE), a multi-agent framework that transforms business knowledge into autonomous, AI-driven competitive advantages.

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