SCMCP

Instructions

Implementation Reference

• src/scmcp/tool/tl.py:164-177 (handler)

  Handler function that dispatches to sc.tl.paga (via tl_func mapping) and executes the core PAGA logic on the AnnData object.
  def run_tl_func(ads, func, arguments): adata = ads.adata_dic[ads.active] if func not in tl_func: raise ValueError(f"Unsupported function: {func}") run_func = tl_func[func] parameters = inspect.signature(run_func).parameters 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 Exception as e: logger.error(f"Error running function {func}: {e}") raise return
• src/scmcp/schema/tl.py:530-556 (schema)

  PAGAModel: Pydantic model defining the input schema and validation for the 'paga' tool parameters.
  class PAGAModel(JSONParsingModel): """Input schema for the Partition-based Graph Abstraction (PAGA) tool.""" groups: Optional[str] = Field( default=None, description="Key for categorical in adata.obs. You can pass your predefined groups by choosing any categorical annotation of observations. Default: The first present key of 'leiden' or 'louvain'." ) use_rna_velocity: bool = Field( default=False, description="Use RNA velocity to orient edges in the abstracted graph and estimate transitions. Requires that adata.uns contains a directed single-cell graph with key ['velocity_graph']." ) model: Literal['v1.2', 'v1.0'] = Field( default='v1.2', description="The PAGA connectivity model." ) neighbors_key: Optional[str] = Field( default=None, description="If specified, paga looks .uns[neighbors_key] for neighbors settings and uses the corresponding connectivities and distances." ) @field_validator('model') def validate_model(cls, v: str) -> str: """Validate model version is supported""" if v not in ['v1.2', 'v1.0']: raise ValueError("model must be either 'v1.2' or 'v1.0'") return v
• src/scmcp/tool/tl.py:75-80 (registration)

  Definition and registration of the 'paga' MCP Tool object with name, description, and input schema.
  # Add paga tool paga_tool = types.Tool( name="paga", description="Partition-based graph abstraction", inputSchema=PAGAModel.model_json_schema(), )
• src/scmcp/tool/tl.py:145-162 (registration)

  tl_tools dictionary registering the 'paga_tool' under the 'paga' key for server lookup.
  tl_tools = { "tsne": tsne_tool, "umap": umap_tool, "draw_graph": draw_graph_tool, "diffmap": diffmap_tool, "embedding_density": embedding_density_tool, "leiden": leiden_tool, "louvain": louvain_tool, "dendrogram": dendrogram_tool, "dpt": dpt_tool, "paga": paga_tool, "ingest": ingest_tool, "rank_genes_groups": rank_genes_groups_tool, "filter_rank_genes_groups": filter_rank_genes_groups_tool, "marker_gene_overlap": marker_gene_overlap_tool, "score_genes": score_genes_tool, "score_genes_cell_cycle": score_genes_cell_cycle_tool, }
• src/scmcp/server.py:35-56 (registration)

  MCP server list_tools() method that includes 'paga' tool via tl_tools.values() for tool discovery.
  @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