SCMCP

Instructions

Implementation Reference

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

  Handler function that executes the 'pca' tool (and other pp tools). It retrieves the active AnnData object, maps 'pca' to sc.pp.pca via pp_func dict, calls it with parsed arguments (forcing inplace=True), logs the operation, and handles errors.
  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:163-218 (schema)

  Pydantic model defining the input schema for the 'pca' tool, including parameters like n_comps, layer, zero_center, svd_solver, etc., with validators.
  class PCAModel(JSONParsingModel): """Input schema for the PCA preprocessing tool.""" n_comps: Optional[int] = Field( default=None, description="Number of principal components to compute. Defaults to 50 or 1 - minimum dimension size.", gt=0 ) layer: Optional[str] = Field( default=None, description="If provided, which element of layers to use for PCA." ) zero_center: Optional[bool] = Field( default=True, description="If True, compute standard PCA from covariance matrix." ) svd_solver: Optional[Literal["arpack", "randomized", "auto", "lobpcg", "tsqr"]] = Field( default=None, description="SVD solver to use." ) mask_var: Optional[Union[str, bool]] = Field( default=None, description="Boolean mask or string referring to var column for subsetting genes." ) dtype: str = Field( default="float32", description="Numpy data type string for the result." ) chunked: bool = Field( default=False, description="If True, perform an incremental PCA on segments." ) chunk_size: Optional[int] = Field( default=None, description="Number of observations to include in each chunk.", gt=0 ) @field_validator('n_comps', 'chunk_size') def validate_positive_integers(cls, v: Optional[int]) -> Optional[int]: """Validate positive integers""" if v is not None and v <= 0: raise ValueError("must be a positive integer") return v @field_validator('dtype') def validate_dtype(cls, v: str) -> str: """Validate numpy dtype""" if v not in ["float32", "float64"]: raise ValueError("dtype must be either 'float32' or 'float64'") return v
• src/scmcp/tool/pp.py:45-49 (registration)

  MCP Tool object registration for 'pca', specifying name, description, and input schema from PCAModel.
  pca = types.Tool( name="pca", description="Principal component analysis", inputSchema=PCAModel.model_json_schema(), )
• src/scmcp/tool/pp.py:88-101 (helper)

  Dictionary mapping tool names to Scanpy functions. 'pca' maps directly to sc.pp.pca, used by run_pp_func to execute the tool.
  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/server.py:34-56 (registration)

  Server's list_tools method that returns the registered tools, including pp_tools which contains the 'pca' tool, based on MODULE environment variable.
  @server.list_tools() async def list_tools() -> list[types.Tool]: if MODULE == "io": tools = io_tools.values() elif MODULE == "pp": tools = pp_tools.values() elif MODULE == "tl": tools = tl_tools.values() elif MODULE == "pl": tools = pl_tools.values() elif MODULE == "util": tools = util_tools.values() else: tools = [ *io_tools.values(), *pp_tools.values(), *tl_tools.values(), *pl_tools.values(), *util_tools.values(), *ccc_tools.values(), ] return tools