MCP Servers Inject Context Tokens on Every Message Even When Not Used
Every configured MCP server injects tokens into the context window on each message, regardless of whether that server is needed for the current task. As developers add more MCP servers, context window bloat becomes severe and reduces effective model capacity. No selective MCP loading mechanism exists to activate servers only when relevant.
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Similar Problems
surfaced semanticallyAll Configured MCP Servers Inject Context Tokens on Every Message Even When Unused
AI development workflows with multiple MCP servers configured experience silent context window bloat because every configured server injects tokens on every message, regardless of whether that server is used. Users have no visibility into which servers are consuming context budget until they notice degraded model performance. No selective activation mechanism exists to enable only the MCP servers relevant to the current task.
AI Coding Tools Consume 24K Tokens on First Message From Injected Cache
AI coding assistants consume approximately 24,000 tokens of context on the very first message due to injected system reminders, MCP tool definitions, and skill instructions. This leaves less context available for actual user interaction.
LM Studio Provider Misreports Context Window Size to Copilot Chat
The LM Studio provider for a coding extension reports a fixed 147K-token context window to VS Code Copilot Chat regardless of the model's actual configured window (e.g., 32,768 tokens), and usage stays stuck at 0%. This prevents Copilot Chat's automatic conversation compaction from triggering as the real context fills up.
Too Many Open Channels Make Slack Navigation Difficult
A user finds that having too many open channels in Slack complicates their ability to navigate and locate the right conversation, without an easy way to manage channel clutter.
Slack Desktop App Lags With High Memory Use Across Multiple Workspaces
Running several Slack workspaces at once drives high memory use and sluggish performance. It affects people who work across organizations.
Problem descriptions, scores, analysis, and solution blueprints may be updated as new community data becomes available.