SCMCP

Instructions

Implementation Reference

• src/scmcp/tool/pp.py:120-136 (handler)

  The handler function that executes the normalize_total tool (and other pp tools) by retrieving the corresponding scanpy function from pp_func and calling it on the active AnnData object with validated arguments and inplace=True.
  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:39-43 (registration)

  Registers the normalize_total tool with MCP types.Tool, specifying 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:88-101 (helper)

  Dictionary mapping tool names to their corresponding scanpy preprocessing functions. The 'normalize_total' key maps to sc.pp.normalize_total, 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/schema/pp.py:576-634 (schema)

  Pydantic-based input schema model for the normalize_total tool, defining parameters and validators.
  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/server.py:67-68 (registration)

  In the MCP server's call_tool handler, dispatches execution of pp tools (including normalize_total) to the run_pp_func handler.
  elif name in pp_tools.keys(): res = run_pp_func(ads, name, arguments)