SCMCP

Instructions

Implementation Reference

• src/scmcp/tool/ccc.py:64-72 (handler)

  Handler function that implements the ccc_circle_plot tool logic: filters results by specificity cutoff, prepares parameters, and calls liana's circle_plot.
  def plot_circleplot(adata, **kwargs): pval = kwargs.pop("specificity_cutoff", 0.05) res_key = kwargs.get("uns_key", "liana_res") pval_col = adata.uns[res_key].columns[-1] kwargs["filter_fun"] = lambda x: x[pval_col] <= pval parameters = inspect.signature( li.pl.circle_plot).parameters kwargs = {k: kwargs.get(k) for k in parameters if k in kwargs} ax = li.pl.circle_plot(adata, **kwargs) return ax
• src/scmcp/schema/ccc.py:181-313 (schema)

  Pydantic model defining the input schema and parameters for the ccc_circle_plot tool.
  class CirclePlotModel(JSONParsingModel): """Input schema for LIANA's circle_plot visualization for cell-cell communication networks.""" uns_key: Optional[str] = Field( default="liana_res", description="Key in adata.uns that contains the LIANA results." ) groupby: Optional[str] = Field( default=None, description="Key to be used for grouping or clustering cells." ) source_key: str = Field( default="source", description="Column name of the sender/source cell types in liana_res." ) target_key: str = Field( default="target", description="Column name of the receiver/target cell types in liana_res." ) score_key: Optional[str] = Field( default=None, description="Column name of the score in liana_res. If None, the score is inferred from the method." ) inverse_score: bool = Field( default=False, description="Whether to invert the score. If True, the score will be -log10(score)." ) top_n: Optional[int] = Field( default=None, description="Top N entities to plot." ) orderby: Optional[str] = Field( default=None, description="If top_n is not None, order the interactions by this column." ) orderby_ascending: Optional[bool] = Field( default=None, description="If top_n is not None, specify how to order the interactions." ) orderby_absolute: bool = Field( default=False, description="If top_n is not None, whether to order by the absolute value of the orderby column." ) source_labels: Optional[Union[List[str], str]] = Field( default=None, description="List of labels to use as source, the rest are filtered out." ) target_labels: Optional[Union[List[str], str]] = Field( default=None, description="List of labels to use as target, the rest are filtered out." ) ligand_complex: Optional[Union[List[str], str]] = Field( default=None, description="List of ligand complexes to filter the interactions to be plotted." ) receptor_complex: Optional[Union[List[str], str]] = Field( default=None, description="List of receptor complexes to filter the interactions to be plotted." ) pivot_mode: Literal["counts", "mean"] = Field( default="counts", description="The mode of the pivot table: 'counts' for number of connections, 'mean' for mean of score values." ) mask_mode: Literal["and", "or"] = Field( default="or", description="The mode of the mask: 'or' to include source or target, 'and' to include source and target." ) specificity_cutoff: float = Field( default=0.05, description="specificity or pval threshold for filtering results. " ) figure_size: Tuple[float, float] = Field( default=(5, 5), description="Figure x,y size." ) edge_alpha: float = Field( default=0.5, description="The transparency of the edges." ) edge_arrow_size: int = Field( default=10, description="The size of the arrow." ) edge_width_scale: Tuple[float, float] = Field( default=(1, 5), description="The scale of the edge width." ) node_alpha: float = Field( default=1.0, description="The transparency of the nodes." ) node_size_scale: Tuple[float, float] = Field( default=(100, 400), description="The scale of the node size." ) node_label_offset: Tuple[float, float] = Field( default=(0.1, -0.2), description="The offset of the node label." ) node_label_size: int = Field( default=8, description="The size of the node label." ) node_label_alpha: float = Field( default=0.7, description="The transparency of the node label." )
• src/scmcp/tool/ccc.py:29-33 (registration)

  MCP Tool object registration with name, description, and schema for ccc_circle_plot.
  circle_plot_tool = types.Tool( name="ccc_circle_plot", description="Visualize cell-cell communication network using a circular plot.", inputSchema=CirclePlotModel.model_json_schema(), )
• src/scmcp/tool/ccc.py:100-106 (registration)

  ccc_tools dictionary that maps tool name to Tool object, including ccc_circle_plot.
  ccc_tools = { "ls_ccc_method": ls_ccc_method_tool, "ccc_rank_aggregate": rank_aggregate_tool, "ccc_circle_plot": circle_plot_tool, "ccc_dot_plot": dot_plot_tool, "ccc": ccc_tool, }
• src/scmcp/server.py:48-56 (registration)

  In list_tools(), includes ccc_tools.values() to register ccc_circle_plot among available tools.
  tools = [ *io_tools.values(), *pp_tools.values(), *tl_tools.values(), *pl_tools.values(), *util_tools.values(), *ccc_tools.values(), ] return tools