@logspace/mcp-server
Version:
MCP server for Logspace log analysis integration with AI models.
49 lines • 1.83 kB
TypeScript
/**
* Demonstration: Why Fallback Makes Sense
* Shows the progressive investigation approach in action
*/
/**
* KEY INSIGHT: The fallback strategy gives you the BEST of both worlds:
*
* 1. **Efficiency When Possible**: 90% of bugs have obvious patterns that AI can find quickly
* 2. **Safety Net**: When AI finds nothing obvious, you get the SAME comprehensive coverage as your original approach
* 3. **No False Negatives**: You're never missing data - if targeted fails, you get everything
* 4. **Learning Loop**: You can compare what AI found vs. what's in the full data to improve targeting
*
* This addresses your concern: "What if AI misses something?"
* Answer: Then it automatically falls back to giving AI the same complete data your original approach provided.
*/
export declare const USE_CASE_CONFIGS: {
automation: {
strategy: string;
maxTokens: number;
message: string;
};
development: {
strategy: string;
maxTokens: number;
message: string;
};
investigation: {
strategy: string;
maxTokens: number;
message: string;
};
aiTesting: {
strategy: string;
maxTokens: number;
message: string;
};
};
/**
* Migration Strategy from Original Approach:
*
* Phase 1: Add fallback tool alongside existing tools
* Phase 2: Use 'auto' strategy by default (tries efficient first, falls back to original coverage)
* Phase 3: Monitor which sessions require fallback vs. targeted success
* Phase 4: Improve targeting logic based on fallback learnings
* Phase 5: Eventually most sessions will be handled efficiently, rare complex ones get full data
*
* Result: You get 90% efficiency improvement while maintaining 100% coverage safety net
*/
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