MCP Context Efficiency: Full-Bundle to On-Demand

When you enable 7 MCP servers, 33.7% of your context is already consumed—before you even start working.

This article shares Claude World Taiwan community’s deep analysis of MCP context efficiency and our proposed solutions.

The Problem: MCP Context Consumption

We measured token consumption for common MCP servers:

MCP Server	Token Cost
GitHub (27 tools)	~18,000
AWS MCP servers	~18,300
Cloudflare	~15,000+
Sentry	~14,000
Playwright (21 tools)	~13,647
Supabase	~12,000+
7 servers total	67,300 (33.7%)

Average: 550-850 tokens per tool.

The Modern Knowledge Worker’s Dilemma

We use multiple platforms simultaneously: GitHub, Jira, Linear, Slack, Vercel, Sentry…

This creates a false choice:

Install all: 50%+ context consumed at session start
Separate by project: Defeats Claude Code’s value as a unified command center

Solution 1: Context Isolation (RFC Proposal)

We submitted RFC #17668 to Anthropic, proposing Context Isolation architecture.

Core Concept

Unlike traditional lazy loading, Context Isolation has a key difference:

Aspect	Traditional Lazy Loading	Context Isolation
Main Context	Loaded when needed, gets polluted	Always stays clean
Load Timing	Runtime dynamic loading	Load at fork creation
Complexity	High (state management)	Low (reuses `context: fork`)

Architecture Design

Main Session (Lean)
 │
 ├── Base MCPs: filesystem, memory
 │   (minimal context footprint)
 │
 ├── Task: database-specialist (forked)
 │   └── Loads: postgres, redis (isolated)
 │
 └── Skill: /deploy (forked)
     └── Loads: vercel, github (isolated)

Implementation

MCP side: Add lazy flag in settings.json

{
  "mcpServers": {
    "memory": { "command": "...", "lazy": false },
    "github": { "command": "...", "lazy": true },
    "postgres": { "command": "...", "lazy": true }
  }
}

Agent/Skill side: Declare required MCPs in frontmatter

---
name: database-specialist
description: Database operations expert
tools: [Read, Bash, Grep]
mcp:
  required: [postgres]
  optional: [redis]
context: fork
---

Why MCP Over Pure Scripts?

MCP’s value isn’t just tools—it’s centralized credential management:

Aspect	MCP	Scripts + .env
Credential Management	Centralized in settings.json	Scattered everywhere
Security	Environment isolation	Risk of log exposure
Token Refresh	Automatic	Manual implementation
Error Handling	Standardized responses	Different per API

Solution 2: Progressive AgentSkill (Community Open Source)

Community member CabLate developed mcp-progressive-agentskill, implementing three-layer progressive disclosure:

Three-Layer Architecture

Layer 1: List available MCP servers (~50-100 tokens)
Layer 2: Show tool names and descriptions for selected server (~200-400 tokens)
Layer 3: Load complete tool specs (~300-500 tokens/tool)

Efficiency Calculation

For an MCP with 20 tools where you need only 2:

Method	Token Cost
Traditional full-bundle	~6,000 tokens
Progressive disclosure	~850 tokens
Savings	86%

Technical Architecture

AI Scripts (Python) → HTTP API → MCP Daemon → MCP Servers

The daemon maintains long-running connections and provides HTTP interface for on-demand tool access.

Quick Start

# Install
python scripts/setup.py

# Start daemon
python scripts/daemon_start.py --no-follow

# List tools
python scripts/mcp_list_tools.py --server playwright

# Call a tool
python scripts/mcp_call.py --server playwright --tool browser_navigate \
  --params '{"url":"https://example.com"}'

Current Recommendations

Until official Context Isolation support arrives:

1. Categorize Your MCPs

Essential (lazy: false):
- filesystem
- memory
- sequential-thinking

Heavy (consider removing or wait for lazy):
- github (18k tokens)
- aws (18k tokens)
- sentry (14k tokens)

2. Use Project Scope

# Enable specific MCP only for projects that need it
claude mcp add --scope project postgres -- ...

3. Try Progressive AgentSkill

For heavy MCP users, CabLate’s solution is available now.

Join the Discussion

RFC Issue: #17668 - Give it a 👍
Open Source: mcp-progressive-agentskill
Community: Discord - Claude World Taiwan

This article is compiled from technical discussions in the Claude World Taiwan community. We’re a group of developers focused on advanced Claude Code usage. Join our Discord to discuss more.