✅ Claude Code Context windows optimization

Hey everyone,

I've been burning a lot of brainpower on my current Leo 2.0 (my PAI) and optimizing my context usage.

TL;DR:Here's how to actually set up Docker's MCP Toolkit if you're trying to optimize your AI agent context usage.

+ The Current Problem +

As you see on my screenshot below, before starting a conversation, I already have 11.7% of my context taken just for the MCP tools (23.3k tokens).

Standard MCP servers dump 13,000-18,000 tokens of tool definitions into your context. When you stack multiple servers, agents get confused and you're burning context on infrastructure instead of actual work.

Docker's approach: containerized MCP servers with a gateway that aggregates everything.

+ Setup Steps (this is a beta feature) +

Prerequisites:

Docker Desktop (not just Docker CLI)
Enable "MCP Toolkit" in Settings → Features in Development (it's Beta)

Enable servers via CLI:

docker mcp server enable github-official
docker mcp server enable filesystem

+ OAuth setup (for services that need it): +

docker mcp oauth authorize github

+ Connect via CLI (once Docker Desktop is running): +

docker mcp client connect claude-code --global

+ Project specific +

docker mcp client connect claude-code

This creates a `.mcp.json` (or updates `~/.claude.json` for global):

+ Verify it works +

Run `/mcp` in Claude Code to see connected servers.

+ What You Actually Get +

Each server runs in its own container (1 CPU, 2GB RAM limit)
One connection point instead of multiple server configs
Browse and add servers from Docker Hub without hunting
Credential management without manual API token juggling

+ My Take So Far +

Still testing, but seems promising: instead of managing 8 separate MCP server configs that each consume tokens, you get one gateway. The question is whether the gateway itself adds overhead that offsets the benefits.

Will update once I've run it through some real workflows.

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