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# Task ID: 46
**Title:** Implement LLM-based prompt selection service
**Status:** done
**Dependencies:** 45 ✓
**Priority:** high
**Description:** Create a service that uses the heavy LLM to intelligently select relevant prompts based on tags and the full prompt index, understanding synonyms and context.
**Details:**
1. Create `/src/mcplocal/src/services/llm-prompt-selector.service.ts`:
```typescript
export class LlmPromptSelectorService {
constructor(
private llmClient: LlmClient,
private fastLlmClient: LlmClient | null,
private tagMatcher: TagMatcherService // fallback
) {}
async selectPrompts(tags: string[], promptIndex: PromptIndex[]): Promise<{
selected: Array<{ name: string; reason: string }>;
priority10: PromptIndex[]; // Always included
}> {
// 1. Extract priority 10 prompts (always included)
// 2. Generate missing summaries using fast LLM if needed
// 3. Send to heavy LLM:
const prompt = `
Given these keywords: ${tags.join(', ')}
And this prompt index:
${promptIndex.map(p => `- ${p.name}: ${p.summary}`).join('\n')}
Select the most relevant prompts for someone working on tasks
related to these keywords. Consider synonyms and related concepts.
Return a ranked JSON array: [{name: string, reason: string}]
`;
// 4. Parse LLM response
// 5. On LLM error, fall back to tag matcher
}
}
```
2. Handle LLM timeouts and errors gracefully with fallback
3. Validate LLM response format
**Test Strategy:**
- Unit test: Priority 10 prompts always returned regardless of LLM selection
- Unit test: LLM called with correct prompt format (mock)
- Unit test: LLM response parsed correctly
- Unit test: Invalid LLM response falls back to tag matcher
- Unit test: LLM timeout falls back to tag matcher
- Unit test: Missing summaries trigger fast LLM generation
- Unit test: No LLM available uses tag matcher directly
- Integration test: End-to-end selection with mock LLM