Math MCP Server for MacOS

python mcp-client.py Starting main execution... Establishing connection to MCP server... Connection established, creating session... Session created, initializing... Requesting tool list... [04/03/25 21:21:07] INFO Processing request of type ListToolsRequest server.py:534 Successfully retrieved 22 tools Creating system prompt... Number of tools: 22 1. add(a: integer, b: integer) - Add two numbers 2. add_list(l: array) - Add all numbers in a list 3. subtract(a: integer, b: integer) - Subtract two numbers 4. multiply(a: integer, b: integer) - Multiply two numbers 5. divide(a: integer, b: integer) - Divide two numbers 6. power(a: integer, b: integer) - Power of two numbers 7. sqrt(a: integer) - Square root of a number 8. cbrt(a: integer) - Cube root of a number 9. factorial(a: integer) - factorial of a number 10. log(a: integer) - log of a number 11. remainder(a: integer, b: integer) - remainder of two numbers divison 12. sin(a: integer) - sin of a number 13. cos(a: integer) - cos of a number 14. tan(a: integer) - tan of a number 15. mine(a: integer, b: integer) - special mining tool 16. create_thumbnail(image_path: string) - Create a thumbnail from an image 17. strings_to_chars_to_int(string: string) - Return the ASCII values of the characters in a word 18. int_list_to_exponential_sum(int_list: array) - Return sum of exponentials of numbers in a list 19. fibonacci_numbers(n: integer) - Return the first n Fibonacci Numbers 20. mac_open_keynote() - Open Keynote on macOS and create a new document. 21. mac_draw_rectangle() - Draw a rectangle in Keynote on macOS from (x1,y1) to (x2,y2). Keynote must be open before calling this tool. 22. mac_add_text_in_keynote(text: string) - Add text in Keynote on macOS inside a rectangle shape. Keynote must be open and rectangle must be drawn before calling this tool. Successfully created tools description Created system prompt... Starting iteration loop... --- Iteration 1 --- Preparing to generate LLM response... Starting LLM generation... LLM generation completed LLM Response: FUNCTION_CALL: strings_to_chars_to_int|INDIA DEBUG: Raw function info: strings_to_chars_to_int|INDIA DEBUG: Split parts: ['strings_to_chars_to_int', 'INDIA'] DEBUG: Function name: strings_to_chars_to_int DEBUG: Raw parameters: ['INDIA'] DEBUG: Found tool: strings_to_chars_to_int DEBUG: Tool schema: {'properties': {'string': {'title': 'String', 'type': 'string'}}, 'required': ['string'], 'title': 'strings_to_chars_to_intArguments', 'type': 'object'} DEBUG: Schema properties: {'string': {'title': 'String', 'type': 'string'}} DEBUG: Converting parameter string with value INDIA to type string DEBUG: Final arguments: {'string': 'INDIA'} DEBUG: Calling tool strings_to_chars_to_int INFO Processing request of type CallToolRequest server.py:534 DEBUG: Raw result: meta=None content=[TextContent(type='text', text='73', annotations=None), TextContent(type='text', text='78', annotations=None), TextContent(type='text', text='68', annotations=None), TextContent(type='text', text='73', annotations=None), TextContent(type='text', text='65', annotations=None)] isError=False DEBUG: Result has content attribute DEBUG: Final iteration result: ['73', '78', '68', '73', '65'] Iteration_result: ['73', '78', '68', '73', '65'] --- Iteration 2 --- Preparing to generate LLM response... Starting LLM generation... LLM generation completed LLM Response: FUNCTION_CALL: int_list_to_exponential_sum|[73, 78, 68, 73, 65] DEBUG: Raw function info: int_list_to_exponential_sum|[73, 78, 68, 73, 65] DEBUG: Split parts: ['int_list_to_exponential_sum', '[73, 78, 68, 73, 65]'] DEBUG: Function name: int_list_to_exponential_sum DEBUG: Raw parameters: ['[73, 78, 68, 73, 65]'] DEBUG: Found tool: int_list_to_exponential_sum DEBUG: Tool schema: {'properties': {'int_list': {'items': {}, 'title': 'Int List', 'type': 'array'}}, 'required': ['int_list'], 'title': 'int_list_to_exponential_sumArguments', 'type': 'object'} DEBUG: Schema properties: {'int_list': {'items': {}, 'title': 'Int List', 'type': 'array'}} DEBUG: Converting parameter int_list with value [73, 78, 68, 73, 65] to type array DEBUG: Final arguments: {'int_list': [73, 78, 68, 73, 65]} DEBUG: Calling tool int_list_to_exponential_sum [04/03/25 21:21:08] INFO Processing request of type CallToolRequest server.py:534 DEBUG: Raw result: meta=None content=[TextContent(type='text', text='7.59982224609308e+33', annotations=None)] isError=False DEBUG: Result has content attribute DEBUG: Final iteration result: ['7.59982224609308e+33'] Iteration_result: ['7.59982224609308e+33'] --- Iteration 3 --- Preparing to generate LLM response... Starting LLM generation... LLM generation completed LLM Response: FUNCTION_CALL: mac_open_keynote DEBUG: Raw function info: mac_open_keynote DEBUG: Split parts: ['mac_open_keynote'] DEBUG: Function name: mac_open_keynote DEBUG: Raw parameters: [] DEBUG: Found tool: mac_open_keynote DEBUG: Tool schema: {'properties': {}, 'title': 'mac_open_keynoteArguments', 'type': 'object'} DEBUG: Schema properties: {} DEBUG: Final arguments: {} DEBUG: Calling tool mac_open_keynote INFO Processing request of type CallToolRequest server.py:534 DEBUG: Raw result: meta=None content=[TextContent(type='text', text='{"content": [{"type": "text", "text": "Keynote opened successfully with a new document"}]}', annotations=None)] isError=False DEBUG: Result has content attribute DEBUG: Final iteration result: ['{"content": [{"type": "text", "text": "Keynote opened successfully with a new document"}]}'] Iteration_result: ['{"content": [{"type": "text", "text": "Keynote opened successfully with a new document"}]}'] --- Iteration 4 --- Preparing to generate LLM response... Starting LLM generation... LLM generation completed LLM Response: FUNCTION_CALL: mac_draw_rectangle DEBUG: Raw function info: mac_draw_rectangle DEBUG: Split parts: ['mac_draw_rectangle'] DEBUG: Function name: mac_draw_rectangle DEBUG: Raw parameters: [] DEBUG: Found tool: mac_draw_rectangle DEBUG: Tool schema: {'properties': {}, 'title': 'mac_draw_rectangleArguments', 'type': 'object'} DEBUG: Schema properties: {} DEBUG: Final arguments: {} DEBUG: Calling tool mac_draw_rectangle [04/03/25 21:21:13] INFO Processing request of type CallToolRequest server.py:534 DEBUG: Raw result: meta=None content=[TextContent(type='text', text='{"content": [{"type": "text", "text": "Rectangle drawn in Keynote from (780,380) to (1140,700)", "annotations": null}]}', annotations=None)] isError=False DEBUG: Result has content attribute DEBUG: Final iteration result: ['{"content": [{"type": "text", "text": "Rectangle drawn in Keynote from (780,380) to (1140,700)", "annotations": null}]}'] Iteration_result: ['{"content": [{"type": "text", "text": "Rectangle drawn in Keynote from (780,380) to (1140,700)", "annotations": null}]}'] --- Iteration 5 --- Preparing to generate LLM response... Starting LLM generation... LLM generation completed LLM Response: FUNCTION_CALL: mac_add_text_in_keynote|7.59982224609308e+33 DEBUG: Raw function info: mac_add_text_in_keynote|7.59982224609308e+33 DEBUG: Split parts: ['mac_add_text_in_keynote', '7.59982224609308e+33'] DEBUG: Function name: mac_add_text_in_keynote DEBUG: Raw parameters: ['7.59982224609308e+33'] DEBUG: Found tool: mac_add_text_in_keynote DEBUG: Tool schema: {'properties': {'text': {'title': 'Text', 'type': 'string'}}, 'required': ['text'], 'title': 'mac_add_text_in_keynoteArguments', 'type': 'object'} DEBUG: Schema properties: {'text': {'title': 'Text', 'type': 'string'}} DEBUG: Converting parameter text with value 7.59982224609308e+33 to type string DEBUG: Final arguments: {'text': '7.59982224609308e+33'} DEBUG: Calling tool mac_add_text_in_keynote [04/03/25 21:21:15] INFO Processing request of type CallToolRequest server.py:534 DEBUG: Raw result: meta=None content=[TextContent(type='text', text='{"content": [{"type": "text", "text": "Text \'7.59982224609308e+33\' added successfully to Keynote", "annotations": null}]}', annotations=None)] isError=False DEBUG: Result has content attribute DEBUG: Final iteration result: ['{"content": [{"type": "text", "text": "Text \'7.59982224609308e+33\' added successfully to Keynote", "annotations": null}]}'] Iteration_result: ['{"content": [{"type": "text", "text": "Text \'7.59982224609308e+33\' added successfully to Keynote", "annotations": null}]}'] --- Iteration 6 --- Preparing to generate LLM response... Starting LLM generation... LLM generation completed LLM Response: FINAL_ANSWER: [7.59982224609308e+33]