Adding multiple full MCP servers creates a phenomenon called "context rot"
When you add multiple MCP servers to your AI, all their tool descriptions load into the AI's context window. This floods the AI with too much information, degrading its performance and decision-making ability.
Most of these tools you don't need for your current task, but they all consume valuable context space.
See the dramatic difference in AI performance and user experience
GitHub + HubSpot + Linear + ...
120+ tool descriptions flood the context window
AI wastes time choosing from generalized tools
AI picks GitHub tools for sales tasks
Manage separate auth for each server
Processing overhead from too many options
Domain-specific tool collection
8-12 targeted tools, zero waste
AI sees exactly what it needs
Right tool, first time, every time
One bundle, centralized auth management
Optimized tool set = faster execution
See the dramatic difference in action across different workflows
Task: "Follow up with leads from last week's campaign"
Task: "Deploy the latest changes and notify the team"
Why AI performs dramatically better with focused tool sets
The Problem: Every MCP server loads tool descriptions into the AI's context window, consuming valuable space.
Focused bundles use only 2,000-3,000 tokens.
Research shows: AI models have optimal performance ranges for tool selection.
Multiple servers = 100+ tools = failure zone.
Bundles eliminate context rot by providing domain-specific, curated tool collections that keep AI in its optimal performance zone.
The difference between frustrating AI experiences and truly productive ones
Context rot slows down every single request
AI makes wrong assumptions across servers
Constant supervision required to catch mistakes
Credential management becomes a nightmare
Clean context enables instant understanding
AI picks the right tool immediately
Minimal supervision, maximum results
Unified credential management