scale

Instructions

Implementation Reference

• src/scmcp/tool/pp.py:120-137 (handler)

  Generic handler that executes sc.pp.scale (via pp_func['scale']) on the active AnnData object for the 'scale' tool.

  def run_pp_func(ads, func, arguments):
      adata = ads.adata_dic[ads.active]
      if func not in pp_func:
          raise ValueError(f"不支持的函数: {func}")
      
      run_func = pp_func[func]
      parameters = inspect.signature(run_func).parameters
      arguments["inplace"] = True
      kwargs = {k: arguments.get(k) for k in parameters if k in arguments}
      try:
          res = run_func(adata, **kwargs)
          add_op_log(adata, run_func, kwargs)
      except KeyError as e:
          raise KeyError(f"Can not foud {e} column in adata.obs or adata.var")
      except Exception as e:
         raise e
      return res

• src/scmcp/schema/pp.py:327-361 (schema)

  Pydantic input schema model for the 'scale' tool, defining parameters and validation.

  class ScaleModel(JSONParsingModel):
      """Input schema for the scale preprocessing tool."""
      
      zero_center: bool = Field(
          default=True,
          description="If False, omit zero-centering variables to handle sparse input efficiently."
      )
      
      max_value: Optional[float] = Field(
          default=None,
          description="Clip (truncate) to this value after scaling. If None, do not clip."
      )
      
      layer: Optional[str] = Field(
          default=None,
          description="If provided, which element of layers to scale."
      )
      
      obsm: Optional[str] = Field(
          default=None,
          description="If provided, which element of obsm to scale."
      )
      
      mask_obs: Optional[Union[str, bool]] = Field(
          default=None,
          description="Boolean mask or string referring to obs column for subsetting observations."
      )
      
      @field_validator('max_value')
      def validate_max_value(cls, v: Optional[float]) -> Optional[float]:
          """Validate max_value is positive if provided"""
          if v is not None and v <= 0:
              raise ValueError("max_value must be positive if provided")
          return v

• src/scmcp/tool/pp.py:63-67 (registration)

  MCP tool registration defining the 'scale' tool name, description, and input schema.

  scale = types.Tool(
      name="scale",
      description="Scale data to unit variance and zero mean",
      inputSchema=ScaleModel.model_json_schema(),
  )

• src/scmcp/tool/pp.py:88-101 (helper)

  Dictionary mapping tool names to Scanpy functions, with 'scale' mapped to sc.pp.scale used by the handler.

  pp_func = {
      "filter_genes": sc.pp.filter_genes,
      "filter_cells": sc.pp.filter_cells,
      "calculate_qc_metrics": partial(sc.pp.calculate_qc_metrics, inplace=True),
      "log1p": sc.pp.log1p,
      "normalize_total": sc.pp.normalize_total,
      "pca": sc.pp.pca,
      "highly_variable_genes": sc.pp.highly_variable_genes,
      "regress_out": sc.pp.regress_out,
      "scale": sc.pp.scale,
      "combat": sc.pp.combat,
      "scrublet": sc.pp.scrublet,
      "neighbors": sc.pp.neighbors,
  }

• src/scmcp/tool/pp.py:104-117 (helper)

  Dictionary of registered MCP tools, including the 'scale' tool instance.

  pp_tools = {
      "filter_genes": filter_genes,
      "filter_cells": filter_cells,
      "calculate_qc_metrics": calculate_qc_metrics,
      "log1p": log1p,
      "normalize_total": normalize_total,
      "pca": pca,
      "highly_variable_genes": highly_variable_genes,
      "regress_out": regress_out,
      "scale": scale,
      "combat": combat,
      "scrublet": scrublet,
      "neighbors": neighbors,
  }