# search-optimization

> Optimize BK search with intent, detail level, and store filtering

- Author: Chris Braddock
- Repository: chris-bluera/bluera-knowledge
- Version: 20260205163644
- Stars: 0
- Forks: 0
- Last Updated: 2026-02-07
- Source: https://github.com/chris-bluera/bluera-knowledge
- Web: https://mule.run/skillshub/@@chris-bluera/bluera-knowledge~search-optimization:20260205163644

---

---
name: search-optimization
description: Optimize BK search with intent, detail level, and store filtering
---

# Optimizing Bluera Knowledge Search

Master the `search()` MCP tool parameters to get better results with less context usage.

## Understanding Search Parameters

```typescript
search(
  query: string,                    // Your search query
  intent?: SearchIntent,            // What you're looking for
  detail?: 'minimal' | 'contextual' | 'full',  // How much context to return
  limit?: number,                   // Max results (default: 10)
  stores?: string[]                 // Which stores to search
)
```

## Search Intent: Choosing the Right Type

The `intent` parameter helps the search engine rank results appropriately for your query type.

### Intent Decision Tree

**Looking for implementation details? Use `find-implementation`**
- "How does X work internally?"
- "Show me the implementation of Y"
- "What's inside the Z class/function?"

```
search("Vue computed properties implementation", intent='find-implementation')
→ Prioritizes: actual class/function implementations
→ Ranks higher: ComputedRefImpl class, createComputed() function
→ Ranks lower: tests, documentation, examples
```

**Looking for usage patterns? Use `find-pattern`**
- "How to use X?"
- "Examples of Y pattern"
- "Common ways to implement Z"

```
search("React hooks patterns", intent='find-pattern')
→ Prioritizes: example code, usage patterns, HOCs
→ Ranks higher: common patterns like useEffect cleanup
→ Ranks lower: internal implementation details
```

**Looking for references? Use `find-usage`**
- "Where is X used?"
- "Find all calls to Y"
- "What depends on Z?"

```
search("useCallback usage", intent='find-usage')
→ Prioritizes: call sites, import statements
→ Ranks higher: files importing and using useCallback
→ Ranks lower: useCallback's own implementation
```

**Looking for definitions/APIs? Use `find-definition`**
- "What is the API for X?"
- "Show me the type definition of Y"
- "What are the parameters for Z?"

```
search("FastAPI route decorator", intent='find-definition')
→ Prioritizes: function signatures, type definitions
→ Ranks higher: @app.get() decorator definition
→ Ranks lower: examples using the decorator
```

**Looking for documentation? Use `find-documentation`**
- "What does the doc say about X?"
- "Explain Y from the documentation"
- "API reference for Z"

```
search("Pydantic validators documentation", intent='find-documentation')
→ Prioritizes: README, docstrings, comments
→ Ranks higher: markdown docs, inline documentation
→ Ranks lower: implementation code
```

### Default (No Intent)

If unsure, omit `intent` - the search engine will use hybrid ranking:

```
search("authentication middleware")
→ Returns mixed: implementations, patterns, usage, docs
→ Balanced ranking across all categories
```

## Detail Level: Progressive Context Strategy

The `detail` parameter controls how much code context is returned **per result**.

### Detail Levels Explained

| Level | What You Get | Tokens/Result | Use When |
|-------|--------------|---------------|----------|
| `minimal` | Summary, file path, relevance | ~100 | Browsing many results |
| `contextual` | + imports, types, signatures | ~300 | Need interface context |
| `full` | + complete code, all context | ~800 | Deep dive on specific file |

### Progressive Detail Strategy (Recommended)

**Step 1: Start Minimal**
```
search(query, detail='minimal', limit=20)
→ Get 20 summaries (~2k tokens total)
→ Scan quickly for relevance
→ Identify top 3-5 candidates
```

**Step 2: Evaluate Scores**
```
Review relevance scores:
- 0.9-1.0: Excellent match (almost certainly relevant)
- 0.7-0.9: Strong match (very likely relevant)
- 0.5-0.7: Moderate match (possibly relevant)
- < 0.5: Weak match (probably not relevant)
```

**Step 3: Selective Deep Dive**
```
For top results (score > 0.7):
  get_full_context(result_ids)
  → Fetch complete code only for relevant items

For moderate results (score 0.5-0.7):
  search(refined_query, detail='contextual')
  → Try different query with more context
```

## Result Limiting

The `limit` parameter caps the number of results returned.

### Choosing the Right Limit

| Limit | Mode | Use When |
|-------|------|----------|
| 20-50 | Discovery | Exploring, not sure what exists |
| 10-20 | Standard | Specific question, multiple files |
| 3-5 | Targeted | Know exactly what you need |

**Tip**: Large limits work best with `detail='minimal'` to control tokens.

## Store Filtering

The `stores` parameter restricts search to specific knowledge stores.

### When to Filter Stores

**✅ Filter when:** You know the library, comparing specific libs, want focused results

**❌ Don't filter when:** Discovering where code lives, want cross-library perspective

```
# Check available stores first
list_stores()

# Then filter
search(query, stores=['fastapi', 'express'])
```

## Quick Reference

### High-Efficiency Defaults
```
search(query, detail='minimal', limit=20)
→ Good for most discovery tasks
→ Review, then selectively fetch full context
```

### High-Precision Defaults
```
search(query, intent='find-implementation', detail='full', limit=5, stores=['known-lib'])
→ When you know exactly what you're looking for
→ Fastest path to deep answer
```

### Balanced Defaults
```
search(query, detail='contextual', limit=10)
→ Good middle ground
→ See interfaces without full implementation
```

## Deep Dive

For advanced strategies and token optimization examples:
- @references/strategies.md - Combined optimization strategies with token counts
- @references/mistakes.md - Common mistakes to avoid