SCMCP

Instructions

Implementation Reference

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

  Generic handler function that executes preprocessing tools including 'neighbors' by calling the corresponding scanpy.pp function (sc.pp.neighbors) with validated arguments and logging the 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/schema/pp.py:498-574 (schema)

  Pydantic model defining the input parameters and validation for the 'neighbors' tool, used in the tool's inputSchema.
  class NeighborsModel(JSONParsingModel): """Input schema for the neighbors graph construction tool.""" n_neighbors: int = Field( default=15, description="Size of local neighborhood used for manifold approximation.", gt=1, le=100 ) n_pcs: Optional[int] = Field( default=None, description="Number of PCs to use. If None, automatically determined.", ge=0 ) use_rep: Optional[str] = Field( default=None, description="Key for .obsm to use as representation." ) knn: bool = Field( default=True, description="Whether to use hard threshold for neighbor restriction." ) method: Literal['umap', 'gauss'] = Field( default='umap', description="Method for computing connectivities ('umap' or 'gauss')." ) transformer: Optional[str] = Field( default=None, description="Approximate kNN search implementation ('pynndescent' or 'rapids')." ) metric: str = Field( default='euclidean', description="Distance metric to use." ) metric_kwds: Dict[str, Any] = Field( default_factory=dict, description="Options for the distance metric." ) random_state: int = Field( default=0, description="Random seed for reproducibility." ) key_added: Optional[str] = Field( default=None, description="Key prefix for storing neighbor results." ) @field_validator('n_neighbors', 'n_pcs') def validate_positive_integers(cls, v: Optional[int]) -> Optional[int]: """Validate positive integers where applicable""" if v is not None and v <= 0: raise ValueError("must be a positive integer") return v @field_validator('method') def validate_method(cls, v: str) -> str: """Validate method is supported""" if v not in ['umap', 'gauss']: raise ValueError("method must be either 'umap' or 'gauss'") return v @field_validator('transformer') def validate_transformer(cls, v: Optional[str]) -> Optional[str]: """Validate transformer option is supported""" if v is not None and v not in ['pynndescent', 'rapids']: raise ValueError("transformer must be either 'pynndescent' or 'rapids'") return v
• src/scmcp/tool/pp.py:81-85 (registration)

  MCP Tool registration for 'neighbors', specifying name, description, and linking to the input schema.
  neighbors = types.Tool( name="neighbors", description="Compute nearest neighbors distance matrix and neighborhood graph", inputSchema=NeighborsModel.model_json_schema(), )
• src/scmcp/tool/pp.py:116-116 (registration)

  Addition of the 'neighbors' tool to the pp_tools dictionary, making it available for server registration.
  "neighbors": neighbors,
• src/scmcp/tool/pp.py:100-100 (helper)

  Mapping of 'neighbors' tool name to the underlying scanpy.pp.neighbors function in the pp_func dictionary used by the handler.
  "neighbors": sc.pp.neighbors,