What is Lfx T2 Wasmedge Mcp Pre Test Attempt

LFX_T2_WasmEdge_MCP_Pre_Test_Attempt is a pre-test code repository for the LFX Term 2 WasmEdge mentorship program, aimed at creating an MCP-based AI agent to assist in LF certification preparation.

Use cases

Use cases include creating AI agents that assist users in finding relevant questions for LF certification preparation, demonstrating MCP capabilities, and integrating external tools through the AI agent.

How to use

To use LFX_T2_WasmEdge_MCP_Pre_Test_Attempt, clone the repository and set up the environment variables for model switching. You can run the project locally using the Dockerized LlamaEdge server for tool-calling capabilities.

Key features

Key features include a modular design for easy addition of new MCP servers, semantic question search using advanced text embedding models, easy model switching via environment variables, and a custom dataset stored in a sqlite3 database.

Where to use

LFX_T2_WasmEdge_MCP_Pre_Test_Attempt can be used in educational and training environments, particularly for preparing for LF certification exams and developing AI agents that utilize MCP.

Pre-test attempt for LFX mentorship (2025/term2) WasmEdge

About

This is a pre-test attempt for the LFX mentorship program, specifically for the “Create an MCP-based AI agent to help LF certificate preparation” [1].

Project Features

• Modular design: The project is designed to be modular, allowing for easy addition of new MCP servers and tools.
• Semantic question search: Rather than using traditional keyword search, the project uses semantic search to find relevant questions. This is done using the gemini’s text-embedding-004 [2] text embedding model and sqlite3 db with sqlite_vec [3] extension to store the vectors.
• Easy model switching: The project allows for easy switching between different models (remote & local), simply by updating the environment variables. Supports LlamaEdge API server [4].
• Custom dataset: The project uses a custom dataset of questions and answers, which is stored in a sqlite3 database. The jsnad_qna.csv dataset is sample dataset created for OpenJS Node.js Application Developer (JSNAD) exam. It is copyright compliant as it is generated using frontier models.

Goal: To create an AI agent that can assist in preparing LF certificates using MCP (Model Context Protocol) [5].

Objective:

• To demonstrate the ability to create an AI agent capable of handling external tools via MCP.
• To demonstrate the understanding of topics like MCP, AI agents, and tool usage.

Dockerized LlamaEdge Server (Optional Local LLM)

For running a local LLM with tool-calling capabilities, a pre-built Docker image for the LlamaEdge API server featuring the Meta Llama 3.2 3B Instruct model is available on Docker Hub:

• Docker Hub Link: mayureshdev/llama-3.2-3b

You can easily pull and run this image to start an OpenAI-compatible chat API server locally or on any machine with Docker. This server supports tool calls, enabling integration with the MCP server.

To run the Dockerized server:

# Pull the image
docker pull mayureshdev/llama-3.2-3b:latest

# Run the container (ensure you have sufficient RAM, ~8GB+ recommended)
docker run -d -p 8080:8080 --name llamaedge-server-3b mayureshdev/llama-3.2-3b:latest

The API will then be available at http://localhost:8080. You can configure the LLM_API_BASE_URL in your .env.local file to http://localhost:8080/v1 and LLM_MODEL to Llama-3.2-3b (or as defined by the server) to use this local LLM.

Detailed instructions for building this and other LlamaEdge Docker images (e.g., for Llama 3.1 8B) can be found in the llamaedge/README.md directory.

Screenshots

Architecture Diagram

flowchart TD
    User([User])
    Client([Client])
    LLM([LLM API Server])
    MCP([MCP Server])
    DB[(SQLite DB<br/>+ Embeddings)]
    CSV[[Q&A Dataset]]

    User -- types query --> Client
    Client -- prompt & tool schema --> LLM
    LLM -- tool call --> Client
    Client -- tool call --> MCP
    MCP -- semantic search --> DB
    DB -- loads from --> CSV
    MCP -- tool result --> Client
    Client -- tool result --> LLM
    LLM -- answer --> Client
    Client -- displays --> User

How to run

It’s very simple to get things up and running. Just follow the steps below:

1. Clone the repository:

   git clone https://github.com/Mayuresh-22/LFX_T2_WasmEdge_MCP_Pre_Test_Attempt.git
   cd LFX_T2_WasmEdge_MCP_Pre_Test_Attempt
   

2. Install the required dependencies:

   uv venv
   .venv/Scripts/activate
   uv sync
   

3. Update the environment variables:

   Add your environment variables to the .env.local.example file
   and rename it to .env.local
   

4. Run the application:

   uv run client.py
   

5. Follow the instructions in the terminal to interact with the AI agent.
6. To stop the chat loop, type “exit” or “quit”.

Pre-test completion status

Original Work & Licensing

This project represents my original work and effort created for the LFX Mentorship (2025/Term2) WasmEdge pre-test. This work is licensed under the MIT License.

Dataset Attribution

The jsnad_qna.csv dataset, used for the OpenJS Node.js Application Developer (JSNAD) exam preparation, was generated using frontier models. The generation process referenced the Node.js Official Documentation, which is available under the MIT License.

• Node.js Official Documentation: https://nodejs.org/api/

Thank You!

Thank you for taking the time to review my pre-test submission. I’m genuinely excited about the possibility of joining the mentorship program and eager to learn, grow, and contribute.
If you have any feedback or suggestions, I’d love to hear them!

Feel free to contact me via [email protected] or LinkedIn.