sg-ai-dev-tools

The SG AI Dev Tools MCP server hosts a hub of resources and guidance for AI development, with a focus on agents, LLMs, GenAI, and MCP-based architectures. It aggregates tutorials, code samples, and best practices around LangChain, Retrieval-Augmented Generation, fine-tuning, and model-context-aware agent design. Once running, you can explore the included resources, execute example workflows, and reference MCP concepts to build and test agent-based solutions. The server is designed to help both newcomers and experienced developers understand how to structure interactions with models and agents using MCP patterns.

Prerequisites:

• Docker installed on your machine (or a compatible container runtime)
• Internet access to pull the container image

Option A: Run with Docker

1. Ensure Docker is running on your system.
2. Start the MCP server: docker run -i jo99112/sg-ai-dev-tools:latest
3. Access the resources at your container's exposed ports if configured (see container logs for any endpoint details).

Option B: Customization (advanced) If you need to customize environment variables or run options, you can extend or override the container run command. For example: docker run -e VAR_NAME=value -p 8080:8080 -i jo99112/sg-ai-dev-tools:latest

Notes:

• The repository URL provides ZIP resources; the container image bundles the MCP resources for quick access.
• If you clone the repository locally, you can inspect the mcp/ directory to understand the resource layout.

Prereqs recap:

• Docker or compatible container runtime
• Basic command line usage familiarity

Tips and common issues:

• If the container fails to start, check the container logs for missing resource files or permission errors.
• Some resources may be large ZIPs; ensure sufficient disk space is available.
• When rewriting config, keep the MCP server name stable (e.g., sg-ai-dev-tools) to avoid confusion across tooling.
• If environment variables are introduced, they should be documented in the repo’s docs or comments in the Dockerfile or startup script.
• This project emphasizes MCP concepts; to experiment, look for examples around agent workflows, tool usage, and context-aware interactions within the provided resources.