ElevenLabs MCP Server

Instructions

Args:
    conversation_id: The unique identifier of the conversation to retrieve, you can get the ids from the list_conversations tool.

Implementation Reference

• elevenlabs_mcp/server.py:703-755 (handler)

  The handler function for the 'get_conversation' MCP tool. It fetches the conversation by ID using the ElevenLabs client, parses the transcript, formats details including metadata and analysis, and returns a TextContent response.

  @mcp.tool(
      description="""Gets conversation with transcript. Returns: conversation details and full transcript. Use when: analyzing completed agent conversations.
  
      Args:
          conversation_id: The unique identifier of the conversation to retrieve, you can get the ids from the list_conversations tool.
      """
  )
  def get_conversation(
      conversation_id: str,
  ) -> TextContent:
      """Get conversation details with transcript"""
      try:
          response = client.conversational_ai.conversations.get(conversation_id)
  
          # Parse transcript using utility function
          transcript, _ = parse_conversation_transcript(response.transcript)
  
          response_text = f"""Conversation Details:
  ID: {response.conversation_id}
  Status: {response.status}
  Agent ID: {response.agent_id}
  Message Count: {len(response.transcript)}
  
  Transcript:
  {transcript}"""
  
          if response.metadata:
              metadata = response.metadata
              duration = getattr(
                  metadata,
                  "call_duration_secs",
                  getattr(metadata, "duration_seconds", "N/A"),
              )
              started_at = getattr(
                  metadata, "start_time_unix_secs", getattr(metadata, "started_at", "N/A")
              )
              response_text += (
                  f"\n\nMetadata:\nDuration: {duration} seconds\nStarted: {started_at}"
              )
  
          if response.analysis:
              analysis_summary = getattr(
                  response.analysis, "summary", "Analysis available but no summary"
              )
              response_text += f"\n\nAnalysis:\n{analysis_summary}"
  
          return TextContent(type="text", text=response_text)
  
      except Exception as e:
          make_error(f"Failed to fetch conversation: {str(e)}")
          # satisfies type checker
          return TextContent(type="text", text="")