okay so this is gonna sound ironic. maybe even a little embarrassing. Most of us — and yes, I'm including myself here — use AI tools to write code every single day. GitHub Copilot, Cursor, Claude, wha
blog.ahmershah.dev11 min read
Really like the line “your codebase is your context.”
One place I’d extend this: AI-readable code helps the agent understand the repo, but agents also need readable execution environments.
That’s where MCP gets interesting. A clean codebase tells the agent what the app is trying to do. An MCP server can tell it what the outside system actually does: available tools, expected inputs, state transitions, webhook outcomes, logs, and failed runs.
For API integrations, this is a big shift. Instead of an agent only reading code + docs and guessing, it can use MCP to run the workflow before editing the code.
So the stack becomes:
code clarity → repo context
MCP → execution context
agent → better changes
We’re exploring this with FetchSandbox MCP for API workflows: giving agents a stateful sandbox they can execute against before touching app code.
Great piece, Ahmer. It’s highly ironic that as we move toward highly autonomous AI agents, the solution isn't fancier prompt engineering ,it’s just going back to the basics of solid, clean code. The point about code consistency across files is huge; humans can context-switch between an API fetch and a Supabase query easily, but it completely breaks an AI's mental model of the codebase.
lily678656
Interesting perspective. I wonder if "AI-readability" will eventually become a standard code review criterion alongside maintainability and performance. As AI tools become more agentic, the quality of context we provide may matter just as much as the prompts we write.