Whissle MCP Server

Instructions

⚠️ COST WARNING: This tool makes an API call to Whissle which may incur costs. Only use when explicitly requested by the user.

Args:
    content (str): The text to summarize
    model_name (str, optional): The LLM model to use. Defaults to "openai"
    instruction (str, optional): Specific instructions for summarization

Returns:
    TextContent with the summary.

Implementation Reference

• whissle_mcp/server.py:384-450 (handler)

  The handler function for the 'summarize_text' tool, decorated with @mcp.tool for registration. It uses the Whissle client's llm_text_summarizer method to generate summaries, includes input validation, logging, retry logic for API errors, and returns a TextContent object with the summary.

  @mcp.tool(
      description="""Summarize text using an LLM model.
  
      ⚠️ COST WARNING: This tool makes an API call to Whissle which may incur costs. Only use when explicitly requested by the user.
  
      Args:
          content (str): The text to summarize
          model_name (str, optional): The LLM model to use. Defaults to "openai"
          instruction (str, optional): Specific instructions for summarization
  
      Returns:
          TextContent with the summary.
      """
  )
  def summarize_text(
      content: str,
      model_name: str = "openai",
      instruction: Optional[str] = None,
  ) -> TextContent:
      try:
          if not content:
              logger.error("Empty content provided for summarization")
              return make_error("Content is required")
          
          # Log the request details
          logger.info(f"Summarizing text using model: {model_name}")
          logger.info(f"Text length: {len(content)} characters")
          
          retry_count = 0
          max_retries = 2  # Increased from 1 to 2
          
          while retry_count <= max_retries:
              try:
                  logger.info(f"Attempting summarization (Attempt {retry_count+1}/{max_retries+1})")
                  response = client.llm_text_summarizer(
                      content=content,
                      model_name=model_name,
                      instruction=instruction,
                  )
                  
                  if response and response.response:
                      logger.info("Summarization successful")
                      return TextContent(
                          type="text",
                          text=f"Summary:\n{response.response}",
                      )
                  else:
                      logger.error("No summary was returned from the API")
                      return make_error("No summary was returned from the API")
              except Exception as api_error:
                  error_msg = str(api_error)
                  logger.error(f"Summarization error: {error_msg}")
                  
                  # Handle API errors with retries
                  error_result = handle_api_error(error_msg, "summarization", retry_count, max_retries)
                  if error_result is not None:  # If we should not retry
                      return error_result  # Return the error message
                  
                  retry_count += 1
          
          # If we get here, all retries failed
          logger.error(f"All summarization attempts failed after {max_retries+1} attempts")
          return make_error(f"Failed to summarize text after {max_retries+1} attempts")
      except Exception as e:
          logger.error(f"Unexpected error during summarization: {str(e)}")
          return make_error(f"Failed to summarize text: {str(e)}")

• whissle_mcp/server.py:384-397 (registration)

  The @mcp.tool decorator registers the 'summarize_text' tool with MCP, including schema description for parameters: content (str), model_name (str, optional), instruction (Optional[str]), and return type TextContent.

  @mcp.tool(
      description="""Summarize text using an LLM model.
  
      ⚠️ COST WARNING: This tool makes an API call to Whissle which may incur costs. Only use when explicitly requested by the user.
  
      Args:
          content (str): The text to summarize
          model_name (str, optional): The LLM model to use. Defaults to "openai"
          instruction (str, optional): Specific instructions for summarization
  
      Returns:
          TextContent with the summary.
      """
  )

• whissle_mcp/server.py:385-397 (schema)

  The tool description provides the input schema: content (required str), model_name (optional str default 'openai'), instruction (optional str), and output as TextContent with summary.

      description="""Summarize text using an LLM model.
  
      ⚠️ COST WARNING: This tool makes an API call to Whissle which may incur costs. Only use when explicitly requested by the user.
  
      Args:
          content (str): The text to summarize
          model_name (str, optional): The LLM model to use. Defaults to "openai"
          instruction (str, optional): Specific instructions for summarization
  
      Returns:
          TextContent with the summary.
      """
  )