SCMCP

Instructions

Implementation Reference

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

  Generic handler function that executes the scrublet tool logic by retrieving sc.pp.scrublet from pp_func mapping, applying it to the active AnnData with provided arguments and inplace=True, handling errors and logging operations.

  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:392-496 (schema)

  Pydantic model defining the input schema and validation for the scrublet tool, including parameters like sim_doublet_ratio, expected_doublet_rate, and validators for positive numbers and distance metrics.

  class ScrubletModel(JSONParsingModel):
      """Input schema for the scrublet doublet prediction tool."""
      
      adata_sim: Optional[str] = Field(
          default=None,
          description="Optional path to AnnData object with simulated doublets."
      )
      
      batch_key: Optional[str] = Field(
          default=None,
          description="Key in adata.obs for batch information."
      )
      
      sim_doublet_ratio: float = Field(
          default=2.0,
          description="Number of doublets to simulate relative to observed transcriptomes.",
          gt=0
      )
      
      expected_doublet_rate: float = Field(
          default=0.05,
          description="Estimated doublet rate for the experiment.",
          ge=0,
          le=1
      )
      
      stdev_doublet_rate: float = Field(
          default=0.02,
          description="Uncertainty in the expected doublet rate.",
          ge=0,
          le=1
      )
      
      synthetic_doublet_umi_subsampling: float = Field(
          default=1.0,
          description="Rate for sampling UMIs when creating synthetic doublets.",
          gt=0,
          le=1
      )
      
      knn_dist_metric: str = Field(
          default="euclidean",
          description="Distance metric used when finding nearest neighbors."
      )
      
      normalize_variance: bool = Field(
          default=True,
          description="Normalize data such that each gene has variance of 1."
      )
      
      log_transform: bool = Field(
          default=False,
          description="Whether to log-transform the data prior to PCA."
      )
      
      mean_center: bool = Field(
          default=True,
          description="Center data such that each gene has mean of 0."
      )
      
      n_prin_comps: int = Field(
          default=30,
          description="Number of principal components used for embedding.",
          gt=0
      )
      
      use_approx_neighbors: Optional[bool] = Field(
          default=None,
          description="Use approximate nearest neighbor method (annoy)."
      )
      
      get_doublet_neighbor_parents: bool = Field(
          default=False,
          description="Return parent transcriptomes that generated doublet neighbors."
      )
      
      n_neighbors: Optional[int] = Field(
          default=None,
          description="Number of neighbors used to construct KNN graph.",
          gt=0
      )
      
      threshold: Optional[float] = Field(
          default=None,
          description="Doublet score threshold for calling a transcriptome a doublet.",
          ge=0,
          le=1
      )
      
      @field_validator('sim_doublet_ratio', 'expected_doublet_rate', 'stdev_doublet_rate',
                     'synthetic_doublet_umi_subsampling', 'n_prin_comps', 'n_neighbors')
      def validate_positive_numbers(cls, v: Optional[Union[int, float]]) -> Optional[Union[int, float]]:
          """Validate positive numbers where applicable"""
          if v is not None and v <= 0:
              raise ValueError("must be a positive number")
          return v
      
      @field_validator('knn_dist_metric')
      def validate_knn_dist_metric(cls, v: str) -> str:
          """Validate distance metric is supported"""
          valid_metrics = ['euclidean', 'manhattan', 'cosine', 'correlation']
          if v.lower() not in valid_metrics:
              raise ValueError(f"knn_dist_metric must be one of {valid_metrics}")
          return v.lower()

• src/scmcp/tool/pp.py:75-79 (registration)

  Registers the scrublet tool as an MCP Tool with name, description, and references the ScrubletModel schema.

  scrublet = types.Tool(
      name="scrublet",
      description="Predict doublets using Scrublet",
      inputSchema=ScrubletModel.model_json_schema(),
  )

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

  Maps the scrublet tool name to the underlying scanpy.pp.scrublet function for execution in 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:103-117 (registration)

  Registers the scrublet Tool object in the pp_tools dictionary, likely used for MCP server tool listing.

  # 模型与函数名称的映射
  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,
  }