Overview
A collection of developer productivity tools and experiments exploring agentic AI patterns, Model Context Protocol (MCP) integrations, and automation pipelines for content workflows and engineering tasks.
Role: Independent engineer — researching, building, and documenting patterns for AI-assisted development.
Problem
Developer workflows involve significant repetitive work: content creation, code scaffolding, documentation updates, and integration testing. Traditional automation scripts are brittle and context-unaware. The emergence of agentic AI patterns (tool-augmented LLMs) creates an opportunity to build more adaptive automation.
Exploration Areas
- How can MCP servers expose development tools to AI agents?
- What patterns make agentic workflows reliable in production?
- Where does automation add genuine value vs. unnecessary complexity?
Solution
MCP Tool Integration
Built custom MCP servers that expose development tools (file operations, API calls, database queries) to AI agents in a standardized protocol. This allows agents to:
- Read and modify codebases with proper context
- Execute development commands with guardrails
- Chain multiple tools together for complex workflows
Content Automation Pipeline
Built automation for LinkedIn content workflows:
- Content drafting with structured prompts and context injection
- Scheduling and publishing via API integrations
- Engagement analytics collection and reporting
Agentic Coding Patterns
Documented and tested patterns for reliable agentic workflows:
- Guard rails: Input validation, output verification, and rollback mechanisms
- Context management: Efficient context window usage, conversation compression
- Tool design: Principles for building tools that agents can use reliably
What I Learned
The key insight is that agentic AI works best when tools are designed for composability and agents are given clear constraints. Unconstrained agents produce unpredictable results; well-scoped agents with focused tool sets are remarkably effective for repetitive engineering tasks.
Automation should be evaluated by time saved per week, not by how impressive it looks. The most valuable automations are often the simplest ones.