learn-agentic-ai

This MCP server provides a learning-focused agentic AI environment inspired by the Learn Agentic AI material. It exposes endpoints and tooling to explore agentic patterns, tool orchestration, and basic A2A interactions in a safe, iterative way. Use the server to experiment with agent planning, tool invocation, and simple workflow composition, guided by the MCP protocol for standardized access to context, tools, and execution results. You can leverage the included examples and scaffolding to prototype small agentic scenarios, observe outcomes, and iterate on guardrails and evaluation criteria to improve ROI-minded delivery.

Typical workflows involve: (1) querying a planning endpoint to generate a course of action, (2) invoking a defined set of tools or services (e.g., data fetch, computation, or messaging), and (3) collecting results for evaluation and adjustment. The MCP-aware components help ensure compatibility with other agents, services, and future NANDA/A2A interoperability work. The environment is designed for hands-on learning, safety checks, and observable telemetry so you can measure impact and iterate toward production-ready patterns.

Prerequisites:

• Node.js (LTS version) and npm installed on your system
• Git installed
• Basic familiarity with command-line tools

Installation steps:

1. Clone the repository: git clone https://github.com/your-org/learn-agentic-ai.git cd learn-agentic-ai

2. Install dependencies: npm install

3. Configure environment (optional):

   • Create a .env file or set environment variables as needed. Example: PORT=3000 ENV=production LOG_LEVEL=info

4. Start the MCP server: npm run start // If a custom start script is not defined, you can run: node server.js

5. Verify it's running:

   • Open http://localhost:3000 (adjust port if you changed PORT)
   • Use any available MCP tooling to query the server's endpoints according to the protocol documentation attached to this repo

Notes:

• If you see port conflicts, stop other services using the same port or change PORT in your environment.
• For development, you can run with DEBUG logs enabled to diagnose issues.

Tips and caveats:

• The server is designed for learning and experimentation with agentic AI concepts; treat it as a sandbox rather than a production-ready system.
• Ensure your MCP tooling aligns with the MCP/A2A/NANDA interoperability goals described in the repo, so future integrations remain smooth.
• If you modify tools or endpoints, update any relevant tool manifests or context mappings so agents can discover and invoke them reliably.
• Common issues include port conflicts, missing dependencies, or misconfigured environment variables. Check the startup logs for hints and ensure required environment variables are set as described above.
• This learning environment emphasizes guardrails, safety checks, and ROI-focused evaluation to help users practice turning AI pilots into measurable value.