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?"

code

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"

code

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?"

code

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?"

code

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"

code

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:

code

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

code

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

code

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

code

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

code

# Check available stores first
list_stores()

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

Quick Reference

High-Efficiency Defaults

code

search(query, detail='minimal', limit=20)
→ Good for most discovery tasks
→ Review, then selectively fetch full context

High-Precision Defaults

code

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

code

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