I Built an MCP Proxy That Reduces Playwright and Figma Context for AI Agents

#ai #opensource #playwright #productivity

Over the past few months I've been using Claude Code heavily with Playwright and Figma.

At first everything worked well, but after longer sessions I started noticing a few recurring problems:

Playwright browser snapshots were consuming hundreds of KB of context.
Figma responses often contained much more information than the agent actually needed.
Browser sessions occasionally crashed, forcing the agent to start over.

None of these problems were caused by the MCP servers themselves.

They were simply returning everything they were designed to return.

The issue was that AI agents pay for every token they receive.

The Idea

Instead of modifying every existing MCP server, I wondered:

What if there was a transparent proxy between the AI agent and the MCP server?

That idea became PlayGuard.

AI Agent
    │
    ▼
PlayGuard
    │
    ▼
Playwright MCP / Figma MCP / ...

The proxy intercepts requests and responses before they reach the agent.

This makes it possible to optimize them without changing the original MCP server.

What PlayGuard Does

Current features include:

Response optimization for Playwright
Response optimization for Figma
Automatic Playwright session recovery
Response caching
Configurable optimization rules
Plugin architecture for additional MCP servers

The goal is simple:

Make existing MCP servers more efficient for AI agents.

Why It Matters

Large language models have limited context windows.

If an MCP server returns 300–500 KB of data every time the agent asks for a browser snapshot, that context disappears quickly.

By removing unnecessary information before it reaches the model, the agent can spend more of its context on reasoning instead of processing redundant data.