Loading Every MCP Server on Every Prompt Quietly Destroys Token Budget
A post on r/ClaudeAI reports a subtle but costly issue: when multiple MCP servers are configured, every prompt loads all of them by default, even trivial queries. The user had 5–6 servers and didn't notice until checking token usage—prompts were burning tokens on loading irrelevant server definitions every single time.
Key Details
- Every prompt loaded the full set of MCP servers (5–6 servers).
- Even simple prompts (e.g., "What time is it?") triggered all server definitions.
- Solution: a custom routing layer that selects only the servers relevant to the prompt.
- Result: token usage dropped significantly, and prompt response times improved.
- The OP admitted they "cannot believe they let it go on that long without checking."
Technical Context
MCP (Model Context Protocol) servers are tools that extend Claude's capabilities (e.g., file system access, database queries, web scraping). The default behavior in many setups—including forked clients and manual configs—is to send the entire list of server definitions with each message. This means tools for DB access, file I/O, web browsing, etc. are all dumped into the context window before the actual user input is processed.
A routing layer can inspect the user's message (or system prompt) and conditionally include only the MCP servers whose descriptions or tools match the intent. For example, a prompt mentioning a file path would activate file tools; a question about stock prices would load only the finance server. This avoids the token overhead of irrelevant server metadata.
Who This Is For
Developers running Claude with multiple MCP servers, especially in automated pipelines or custom frontends where token efficiency matters.
📖 Read the full source: r/ClaudeAI
👀 See Also
OpenClaw LLM Timeout Fix for Cold Model Loading
A Reddit user identified and fixed a specific timeout issue in OpenClaw where cold-loaded local LLMs would fail after about 60 seconds, even with higher general timeouts set. The solution involves adjusting the embedded-runner LLM idle timeout configuration.
Claude AI Users Getting Better Results by Providing Context Instead of Generic Prompts
A Reddit discussion highlights that users getting real work done with Claude AI provide specific context about their situation, what they've tried, what good looks like, and what to avoid, rather than treating it like a search engine.
Claude Prompt for Visualizing Thinking Structure: Intent, Reality, Gap
A Reddit user shares a 100-word prompt for Claude that asks the AI to notice and reflect back the structural patterns in conversation—categorized as Intent (what you WANT), Reality (what IS), and Gap (what's UNRESOLVED)—rather than the content itself.
Community Discusses Solutions for OpenClaw Token Consumption
Users share strategies for managing high token usage when running AI agents around the clock.