Find all conversations about a specific topic across your entire meeting history.

Example:

"Search my Fireflies transcripts for all mentions of
 'API integration' from the last quarter"

Natural language workflow:

• AI uses fireflies_search_transcripts with keyword “API integration”
• Filters to Q4 date range
• Returns all matching transcripts with context snippets
• Shows where and when the topic was discussed

What you get:

• All meetings discussing the topic
• Context snippets showing usage
• Speaker attribution per mention
• Chronological topic tracking

Find customer mentions or feedback across all client conversations.

Example:

"Search for all mentions of 'customer retention' or
 'churn' in my client meetings"

Natural language workflow:

• Searches transcripts for keywords
• Returns all matching meetings
• Shows customer feedback in context
• Identifies patterns and trends

What you get:

• Customer sentiment on topics
• Feature requests and pain points
• Recurring themes
• Feedback timeline

Track competitor mentions across sales conversations.

Example:

"Find all my sales calls where prospects mentioned
 competitor names"

Natural language workflow:

• Searches for competitor names
• Returns relevant conversations
• Shows competitive context
• Identifies objection patterns

What you get:

• Competitive mention frequency
• Context of competitor discussions
• Common comparisons
• Competitive positioning insights

Find conversations about specific topics, then create minds from those discussions.

Example:

"Search for all meetings discussing 'enterprise security',
 then create minds for the customers who raised those concerns"

Natural language workflow:

1. Searches for “enterprise security” keyword
2. Retrieves full transcripts for matching meetings
3. Identifies customer speakers from those segments
4. Creates minds for each customer focused on security discussions
5. Trains on their security-related segments

What you get:

• Topic-focused minds
• Customer-specific security concerns
• Targeted simulation capabilities
• Context-aware digital twins

1
{
2
  "error": "Bad Request",
3
  "message": "Keyword parameter is required"
4
}

Solution: Provide a keyword parameter with your search query.

1
{
2
  "error": "Unauthorized",
3
  "message": "Fireflies authentication failed"
4
}

Solution: Verify your Fireflies API key is configured correctly.

1
{
2
  "error": "Rate Limit Exceeded",
3
  "message": "Too many search requests"
4
}

Solution: Wait before retrying. Reduce search frequency or use more specific keywords.

1
{
2
  "results": [],
3
  "total": 0,
4
  "hasMore": false
5
}

Solution: Not an error—no transcripts contain the keyword. Try:

• Using different or broader keywords
• Checking for typos
• Widening the date range
• Using partial matches or variations

• Use specific, relevant keywords for best results
• Try variations of terms (e.g., “pricing”, “price”, “cost”)
• Use phrases for more precise matches (e.g., “enterprise features”)
• Combine search with date filtering for targeted results

• Start with broad keywords, then refine
• Use date ranges to narrow large result sets
• Review snippets before retrieving full transcripts
• Save transcript IDs for later retrieval

• Search for topics to find relevant conversations
• Retrieve full transcripts for matches
• Create topic-specific minds from search results
• Build knowledge bases around themes

• Specific keywords return faster than generic ones
• Use date filters to reduce search scope
• Limit results to only what you need
• Review snippets to avoid unnecessary transcript downloads