Optimizing Bluera Knowledge Search

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

Understanding Search Parameters

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
)

Each parameter affects results and token usage. Choose wisely!

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

Examples by Use Case

Use Case: Quick Discovery

"I need to find something but not sure where it is"

search("websocket handling", detail='minimal', limit=30)
→ Browse 30 summaries quickly
→ Total: ~3k tokens
→ Find general location

Then:
get_full_context(top_3_ids)
→ Deep dive on relevant files
→ Additional: ~2.5k tokens
→ Total context: ~5.5k tokens (vs ~24k if detail='full' upfront)

Use Case: API Reference

"I need to see function signatures and types"

search("route decorator", detail='contextual', limit=10)
→ Get imports, types, signatures
→ Total: ~3k tokens
→ See API without full implementation

Usually enough! Only get full if needed.

Use Case: Deep Implementation Study

"I know exactly what I'm looking for, need complete code"

search("ComputedRefImpl class", detail='full', limit=3, stores=['vue'])
→ Get complete implementation immediately
→ Total: ~2.5k tokens
→ Everything you need in one call

Result Limiting

The limit parameter caps the number of results returned.

Choosing the Right Limit

Large limit (20-50): Discovery mode

• You're exploring, not sure what exists
• Willing to browse many summaries
• Use with detail='minimal' to keep tokens reasonable

search("error handling patterns", detail='minimal', limit=40)
→ Cast wide net
→ Browse many options
→ ~4k tokens total

Medium limit (10-20): Standard search

• You have a specific question
• Expect multiple relevant files
• Balance between coverage and context

search("authentication middleware", detail='minimal', limit=15)
→ Good coverage
→ Not overwhelming
→ ~1.5k tokens

Small limit (3-5): Targeted search

• You know what you're looking for
• Just need the best matches
• Often combined with detail='full'

search("class ComputedRefImpl", detail='full', limit=3, stores=['vue'])
→ Precise target
→ Complete code immediately
→ ~2.5k tokens

Store Filtering

The stores parameter restricts search to specific knowledge stores.

When to Filter Stores

✅ Use store filtering when:

• You know which library/codebase has the answer
• Comparing specific libraries (e.g., Express vs Fastapi)
• You want more results from a specific source

# Focused search
search("routing", stores=['fastapi'], limit=15)
→ More FastAPI-specific results
→ Higher quality for that framework

# Comparative search
search("middleware implementation", stores=['express', 'hono'], limit=10)
→ Get perspective from both frameworks
→ Balanced results

❌ Don't filter when:

• You're discovering which library has the answer
• You want cross-library perspectives
• You're not sure where the code lives

# Let search find the best match across all libraries
search("dependency injection patterns", limit=15)
→ Might find great examples in FastAPI, NestJS, or Angular
→ Don't limit yourself prematurely

Listing Available Stores

Before filtering, know what's indexed:

list_stores()
→ See all available stores
→ Note store names for filtering

Then:
search(query, stores=['store1', 'store2'])

Combined Optimization Strategies

Strategy 1: Efficient Discovery

Goal: Find something across many files, minimize tokens

1. search(query, detail='minimal', limit=30)
   → Browse summaries (~3k tokens)

2. Filter top 5 by score (>0.7)

3. get_full_context(top_5_ids)
   → Deep dive selectively (~4k tokens)

Total: ~7k tokens (vs ~24k with detail='full' upfront)
Savings: ~70% token reduction

Strategy 2: Precise Targeting

Goal: Get exactly what you need, fast

1. Identify store: list_stores()

2. search(precise_query,
         intent='find-implementation',
         detail='full',
         limit=3,
         stores=['target-store'])
   → Exact match with full code

Total: ~2.5k tokens
Result: Fastest path to answer

Strategy 3: Comparative Analysis

Goal: Compare implementations across libraries

1. search(query,
         intent='find-implementation',
         detail='minimal',
         limit=20,
         stores=['lib1', 'lib2', 'lib3'])
   → Get summaries from multiple libraries

2. Review distribution:
   - lib1: 8 results
   - lib2: 7 results
   - lib3: 5 results

3. get_full_context(top_2_from_each_lib)
   → Compare implementations

Total: ~5k tokens
Result: Balanced cross-library comparison

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

Common Mistakes to Avoid

1. ❌ Using detail='full' with limit=50

   • Result: ~40k tokens consumed
   • Fix: Start with detail='minimal', escalate selectively

2. ❌ Not using intent parameter

   • Result: Mixed ranking, less relevant results
   • Fix: Choose intent based on query type

3. ❌ Over-filtering stores too early

   • Result: Miss better answers in other libraries
   • Fix: Search broadly first, then narrow

4. ❌ Setting limit too low (1-2)

   • Result: Miss relevant alternatives
   • Fix: Use limit=5 minimum, more for discovery

5. ❌ Not checking relevance scores

   • Result: Waste time on low-relevance results
   • Fix: Filter by score > 0.7 before deep dive

Token Usage Examples

Real token counts for different strategies:

# Inefficient approach
search("auth middleware", detail='full', limit=30)
→ 30 results × 800 tokens = 24,000 tokens
→ Most results not even relevant!

# Optimized approach
search("auth middleware", detail='minimal', limit=30)
→ 30 results × 100 tokens = 3,000 tokens
→ Identify top 3 (score > 0.8)

get_full_context([id1, id2, id3])
→ 3 results × 800 tokens = 2,400 tokens

Total: 5,400 tokens (78% reduction!)

Master these optimization strategies to search faster, use less context, and get better results.