Airweave Search

Use this skill to effectively search and retrieve context from Airweave collections, whether answering questions or gathering context to complete tasks.

When to Search

Search when the user:

• Asks about data in their connected apps ("What did we discuss in Slack about...")
• Needs to find documents, messages, issues, or records
• Asks factual questions about their workspace ("Who is responsible for...", "What's our policy on...")
• References specific tools by name ("in Notion", "on GitHub", "in Jira")
• Needs recent information you don't have in your training
• Needs you to check app data for context to complete a task ("check our Notion docs", "look at the Jira ticket", "see what we decided in Slack")

Don't search when:

• User asks general knowledge questions (use your training)
• User is asking how to SET UP Airweave (use airweave-setup skill instead)
• User already provided all needed context in the conversation
• The question is about Airweave itself, not data within it

Search Modes

Airweave provides three search modes. Choose based on user intent:

Mode	When to Use	Speed
instant	Simple lookups, exact term matching, browsing	Fastest
classic	Most searches — AI generates an optimized search plan	Fast
agentic	Complex questions requiring reasoning, multi-step retrieval	Slower, highest quality

Mode Selection Guide

User Intent	Mode
Quick document lookup	instant
Finding specific information	classic (default)
General topic exploration	classic
"Summarize", "analyze", "compare"	agentic
Complex multi-source questions	agentic
Simple keyword search	instant with retrieval_strategy: "keyword"

Query Formulation

Extract Key Concepts

Turn user intent into effective search queries:

User Says	Search Query
"What did Sarah say about the launch?"	"Sarah product launch"
"Find the API documentation"	"API documentation"
"Any bugs reported this week?"	"bug report issues"
"What's our refund policy?"	"refund policy customer"

Query Tips

1. Use natural language - Airweave uses semantic search, not keyword matching
2. Include context - "pricing feedback" is better than just "pricing"
3. Be specific but not too narrow - Start moderately specific, broaden if no results
4. Avoid filler words - Skip "please find", "can you search for"

Parameter Quick Reference

Parameter	Values	When to Use
mode	instant/classic/agentic	instant for speed, classic for most searches, agentic for complex reasoning
limit	1-1000	Lower (5-10) for quick answers, higher (20-50) for exploration
offset	0+	Pagination (instant/classic only)
retrieval_strategy	hybrid/neural/keyword	Instant mode only: keyword for exact terms, neural for concepts, hybrid (default) for both
thinking	boolean	Agentic mode only: enable extended reasoning for complex queries

See PARAMETERS.md for detailed guidance.

Handling Results

Interpreting Scores

Results include a relevance_score field:

Score	Meaning	Action
0.85+	Highly relevant	Use confidently
0.70-0.85	Likely relevant	Use with context
0.50-0.70	Possibly relevant	Mention uncertainty
Below 0.50	Weak match	Consider rephrasing query

Understanding Result Structure

Each result contains:

• name — Document/entity title
• textual_representation — The full text content
• breadcrumbs — Hierarchy path (e.g., Workspace > Channel > Message)
• airweave_system_metadata.source_name — Source app (e.g., "Slack", "Notion")
• web_url — Link back to the original item
• created_at / updated_at — Timestamps

Synthesizing Answers

When presenting results to users:

1. Lead with the answer - Don't start with "I found 5 results"
2. Cite sources - Mention where info came from ("According to your Slack conversation...")
3. Use breadcrumbs - Reference the hierarchy path for context ("In the Engineering > API Design channel...")
4. Synthesize, don't dump - Combine relevant parts into coherent response
5. Acknowledge gaps - If results don't fully answer, say so

Handling No/Poor Results

If search returns no results or low-quality matches:

1. Try a different mode - Switch from instant to classic, or classic to agentic
2. Broaden the query - Remove specific terms, use more general concepts
3. Try different phrasing - Rephrase using synonyms or related terms
4. Increase limit - Fetch more results to find relevant matches
5. Check source availability - The data source might not be connected
6. Ask for clarification - User might have more context to share

Finding the Search Tool

Airweave MCP tools follow the naming pattern search-{collection-name}. Look for tools matching this pattern in your available MCP tools.

Examples:

• search-acmes-slack-k8v2x1
• search-acmes-notion-p3m9q7
• search-acmes-jira-w5n4r2

If no Airweave search tool is available:

• The user may not have Airweave MCP configured
• Ask if they have Airweave set up and connected to their AI assistant
• Suggest using the airweave-setup skill for configuration help

Multiple collections: If multiple search-* tools are available, choose based on the collection name and the user's request. If unclear which to use, ask the user or try the most general-sounding one first.

Calling the Search Tool

Use the search-{collection} MCP tool with your chosen parameters:

search-acmes-slack-k8v2x1({
  query: "customer feedback pricing",
  mode: "classic",
  limit: 10
})

search-acmes-notion-p3m9q7({
  query: "API authentication docs",
  mode: "instant",
  retrieval_strategy: "hybrid"
})

search-acmes-jira-w5n4r2({
  query: "What decisions were made about the refund policy?",
  mode: "agentic"
})

Examples

See EXAMPLES.md for complete conversation examples showing effective search patterns.