Fermat MCP

Instructions

Implementation Reference

• fmcp/numpy_mcp/core/matlib.py:5-104 (handler)

  The handler function implementing the 'matlib_operation' tool for various matrix library operations using NumPy, such as creating random matrices, zeros, ones, eye, arange, linspace, reshape, etc.
  def matlib_operation( operation: str, data: Optional[Union[list, int, float]] = None, shape: Optional[Union[list, int]] = None, m: Optional[int] = None, n: Optional[int] = None, k: int = 0, start: Optional[float] = None, stop: Optional[float] = None, step: Optional[float] = None, num: Optional[int] = None, axis: int = 0, ) -> list: """ Unified interface for numerical matrix operations using numpy. Args: operation: The matrix operation to perform. One of: - 'rand-mat': Matrix of random values (0-1) (m: int, n: int) -> 2D array - 'zeros': Matrix of zeros (shape: list) -> array - 'ones': Matrix of ones (shape: list) -> array - 'eye': 2D array with ones on diagonal (m: int, n: int, k: int) -> 2D array - 'identity': Identity matrix (n: int) -> 2D array - 'arange': Evenly spaced values (start: float, stop: float, step: float) -> 1D array - 'linspace': Evenly spaced numbers (start: float, stop: float, num: int) -> 1D array - 'reshape': Reshape array (data: list, shape: list) -> array - 'flatten': Flatten array (data: list) -> 1D array - 'concatenate': Join arrays (data: list[list], axis: int) -> array - 'transpose': Transpose array (data: list) -> array - 'stack': Stack arrays (data: list[list], axis: int) -> array data: Input data for matrix operations shape: Shape of the output matrix m: First dimension n: Second dimension k: Diagonal offset for eye operation start: Start value for arange/linspace stop: Stop value for arange/linspace step: Step size for arange num: Number of samples for linspace axis: Axis along which to perform operations Returns: list: The resulting matrix/array as a nested list """ if operation == "zeros": return np.zeros(shape).tolist() elif operation == "ones": return np.ones(shape).tolist() elif operation == "eye": return np.eye(m or n, n, k).tolist() elif operation == "identity": return np.eye(n).tolist() elif operation == "rand-mat": return np.random.rand(m or 1, n or (m or 1)).tolist() elif operation == "arange": if start is None or stop is None: raise ValueError("start and stop are required for arange operation") if step is None: return np.arange(start, stop).tolist() return np.arange(start, stop, step).tolist() elif operation == "linspace": if start is None or stop is None or num is None: raise ValueError("start, stop, and num are required for linspace operation") return np.linspace(start, stop, num).tolist() elif operation == "reshape": if data is None or shape is None: raise ValueError("data and shape are required for reshape operation") return np.array(data).reshape(shape).tolist() elif operation == "flatten": if data is None: raise ValueError("data is required for flatten operation") return np.array(data).flatten().tolist() elif operation == "concatenate": if data is None: raise ValueError("data is required for concatenate operation") return np.concatenate([np.array(x) for x in data], axis=axis).tolist() elif operation == "transpose": if data is None: raise ValueError("data is required for transpose operation") return np.array(data).T.tolist() elif operation == "stack": if data is None: raise ValueError("data is required for stack operation") return np.stack([np.array(x) for x in data], axis=axis).tolist() else: raise ValueError(f"Unknown operation: {operation}")
• fmcp/numpy_mcp/server.py:18-21 (registration)

  Registration of the 'matlib_operation' tool in the numpy_mcp FastMCP server instance.
  numpy_mcp.tool( matlib_operation, description="Do matrix operations: rand-mat, zeros, ones, eye, identity, arange, linspace, reshape, flatten, concatenate, transpose, stack", )
• fmcp/numpy_mcp/server.py:3-3 (registration)

  Import of the matlib_operation handler function.
  from fmcp.numpy_mcp.core.matlib import matlib_operation
• fmcp/numpy_mcp/core/matlib.py:5-49 (schema)

  Function signature and docstring defining the input schema and supported operations for the tool.
  def matlib_operation( operation: str, data: Optional[Union[list, int, float]] = None, shape: Optional[Union[list, int]] = None, m: Optional[int] = None, n: Optional[int] = None, k: int = 0, start: Optional[float] = None, stop: Optional[float] = None, step: Optional[float] = None, num: Optional[int] = None, axis: int = 0, ) -> list: """ Unified interface for numerical matrix operations using numpy. Args: operation: The matrix operation to perform. One of: - 'rand-mat': Matrix of random values (0-1) (m: int, n: int) -> 2D array - 'zeros': Matrix of zeros (shape: list) -> array - 'ones': Matrix of ones (shape: list) -> array - 'eye': 2D array with ones on diagonal (m: int, n: int, k: int) -> 2D array - 'identity': Identity matrix (n: int) -> 2D array - 'arange': Evenly spaced values (start: float, stop: float, step: float) -> 1D array - 'linspace': Evenly spaced numbers (start: float, stop: float, num: int) -> 1D array - 'reshape': Reshape array (data: list, shape: list) -> array - 'flatten': Flatten array (data: list) -> 1D array - 'concatenate': Join arrays (data: list[list], axis: int) -> array - 'transpose': Transpose array (data: list) -> array - 'stack': Stack arrays (data: list[list], axis: int) -> array data: Input data for matrix operations shape: Shape of the output matrix m: First dimension n: Second dimension k: Diagonal offset for eye operation start: Start value for arange/linspace stop: Stop value for arange/linspace step: Step size for arange num: Number of samples for linspace axis: Axis along which to perform operations Returns: list: The resulting matrix/array as a nested list """