Intro To Mcps
A repo for me to learn about mcps and build some on the way
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intro-to-mcps
A hands-on learning repository for understanding the Model Context Protocol (MCP) and building agentic systems from first principles.
This repository is a structured experimentation lab for:
- Creating MCP servers
- Connecting LLMs to structured tools
- Designing simple multi-role development swarms
- Understanding the tool invocation lifecycle
- Exploring persona-driven AI workflows
This is not a production framework. It is a mechanics workshop.
Why This Repository Exists
Most modern agent frameworks abstract away the important parts.
This project intentionally exposes:
- The reasoning loop
- Tool registration and invocation
- The separation between LLM and agent
- The orchestration logic
- The limits of autonomous behavior
If something works, you can trace exactly why it worked.
If something breaks, you can see where.
Repository Structure
first-steps/ Basic experiments and API exploration. Small scripts to understand core MCP and LLM interactions.
development-swarm/ A simulated multi-role development workflow.
develop.py Main orchestration loop.
mcps/ Tool definitions exposed via MCP: - dev_tools.py - file_tools.py - qa_tools.py - run_tools.py
personas/ Role definitions that shape LLM behavior: - developer.md - manager.md - tester.md
dev-space/ Working directory where generated code and tests are written.
utils.py Shared helpers used across the swarm.
requirements.txt Python dependencies.
Learning Objectives
This repository explores answers to:
- What actually interacts with an MCP server?
- How does an LLM decide to call a tool?
- What is the difference between an LLM and an agent?
- How do multiple personas coordinate via a single orchestration loop?
- How can tools enforce structure and reliability?
How the Development Swarm Works
- A manager persona defines tasks.
- A developer persona writes code.
- A QA persona tests the implementation.
- MCP tools perform file writes, test execution, and command runs.
- The orchestrator coordinates the cycle until completion.
Every action flows through explicit tool calls. There is no hidden automation layer.
Core Concepts Practiced
- Tool registration through MCP
- Structured tool invocation
- Role-based prompting
- Local file manipulation through controlled interfaces
- Test-driven iteration via tool feedback
- Observing the reasoning loop in action
What This Is Not
- A polished framework
- A drop-in agent solution
- A cloud-based automation platform
It is a learning scaffold.
Next Step
For a cleaner, local-first, Ollama-driven evolution of these ideas, see the mini-claw repository.
