ElevenLabs MCP Server

Instructions

Implementation Reference

• elevenlabs_mcp/server.py:770-814 (handler)

  The main handler function that implements the 'get_conversation' tool logic. It retrieves conversation details and transcript from the ElevenLabs API, parses the transcript, and formats a comprehensive text response including metadata and analysis.
  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="")
• elevenlabs_mcp/server.py:763-769 (registration)

  Registers the 'get_conversation' tool with the MCP framework using the @mcp.tool decorator. The description provides usage info and argument documentation, serving as implicit schema.
  @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. """ )
• elevenlabs_mcp/utils.py:177-226 (helper)

  Helper utility function used by get_conversation to parse and format the conversation transcript, handling long transcripts by saving them to a temporary file.
  def parse_conversation_transcript(transcript_entries, max_length: int = 50000): """ Parse conversation transcript entries into a formatted string. If transcript is too long, save to temporary file and return file path. Args: transcript_entries: List of transcript entries from conversation response max_length: Maximum character length before saving to temp file Returns: tuple: (transcript_text_or_path, is_temp_file) """ transcript_lines = [] for entry in transcript_entries: speaker = getattr(entry, "role", "Unknown") text = getattr(entry, "message", getattr(entry, "text", "")) timestamp = getattr(entry, "timestamp", None) if timestamp: transcript_lines.append(f"[{timestamp}] {speaker}: {text}") else: transcript_lines.append(f"{speaker}: {text}") transcript = ( "\n".join(transcript_lines) if transcript_lines else "No transcript available" ) # Check if transcript is too long for LLM context window if len(transcript) > max_length: # Create temporary file temp_file = tempfile.SpooledTemporaryFile( mode="w+", max_size=0, encoding="utf-8" ) temp_file.write(transcript) temp_file.seek(0) # Get a persistent temporary file path with tempfile.NamedTemporaryFile( mode="w", suffix=".txt", delete=False, encoding="utf-8" ) as persistent_temp: persistent_temp.write(transcript) temp_path = persistent_temp.name return ( f"Transcript saved to temporary file: {temp_path}\nUse the Read tool to access the full transcript.", True, ) return transcript, False