SCMCP

Instructions

Implementation Reference

• src/scmcp/schema/pp.py:576-634 (schema)

  Pydantic input schema model (NormalizeTotalModel) for the normalize_total tool, including validation for parameters like target_sum and max_fraction.
  class NormalizeTotalModel(JSONParsingModel): """Input schema for the normalize_total preprocessing tool.""" target_sum: Optional[float] = Field( default=None, description="If None, after normalization, each cell has a total count equal to the median of total counts before normalization. If a number is provided, each cell will have this total count after normalization." ) exclude_highly_expressed: bool = Field( default=False, description="Exclude highly expressed genes for the computation of the normalization factor for each cell." ) max_fraction: float = Field( default=0.05, description="If exclude_highly_expressed=True, consider cells as highly expressed that have more counts than max_fraction of the original total counts in at least one cell.", gt=0, le=1 ) key_added: Optional[str] = Field( default=None, description="Name of the field in adata.obs where the normalization factor is stored." ) layer: Optional[str] = Field( default=None, description="Layer to normalize instead of X. If None, X is normalized." ) layers: Optional[Union[Literal['all'], List[str]]] = Field( default=None, description="List of layers to normalize. If 'all', normalize all layers." ) layer_norm: Optional[str] = Field( default=None, description="Specifies how to normalize layers." ) inplace: bool = Field( default=True, description="Whether to update adata or return dictionary with normalized copies." ) @field_validator('target_sum') def validate_target_sum(cls, v: Optional[float]) -> Optional[float]: """Validate target_sum is positive if provided""" if v is not None and v <= 0: raise ValueError("target_sum must be positive") return v @field_validator('max_fraction') def validate_max_fraction(cls, v: float) -> float: """Validate max_fraction is between 0 and 1""" if v <= 0 or v > 1: raise ValueError("max_fraction must be between 0 and 1") return v
• src/scmcp/tool/pp.py:39-43 (registration)

  MCP Tool registration: defines the normalize_total tool with name, description, and input schema from NormalizeTotalModel.
  normalize_total = types.Tool( name="normalize_total", description="Normalize counts per cell to the same total count", inputSchema=NormalizeTotalModel.model_json_schema(), )
• src/scmcp/tool/pp.py:120-137 (handler)

  Handler function that executes the normalize_total logic: retrieves adata, maps func name to sc.pp.normalize_total, calls it with validated args, logs operation.
  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/tool/pp.py:88-101 (helper)

  Helper dictionary mapping tool name 'normalize_total' to the underlying scanpy.pp.normalize_total function.
  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 (registration)

  Registration of normalize_total tool in pp_tools dictionary, exported via tool/__init__.py for use in MCP server.
  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, }