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pl_stacked_violin

Visualize single-cell RNA sequencing data distributions by creating compact stacked violin plots to compare gene expression across cell groups.

Instructions

Plot stacked violin plots. Makes a compact image composed of individual violin plots stacked on top of each other.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
figsizeNoFigure size. Format is (width, height).
color_mapNoColor map to use for continuous variables.
paletteNoColors to use for plotting categorical annotation groups.
vmaxNoThe value representing the upper limit of the color scale.
vminNoThe value representing the lower limit of the color scale.
vcenterNoThe value representing the center of the color scale.
legend_fontsizeNoNumeric size in pt or string describing the size.
legend_fontweightNoLegend font weight. A numeric value in range 0-1000 or a string.bold
legend_locNoLocation of legend, either 'on data', 'right margin' or a valid keyword for the loc parameter.right margin
legend_fontoutlineNoLine width of the legend font outline in pt.
groupbyNoThe key of the observation grouping to consider.
logNoPlot on logarithmic axis.
use_rawNoUse raw attribute of adata if present.
var_namesNovar_names should be a valid subset of adata.var_names.
layerNoName of the AnnData object layer that wants to be plotted.
gene_symbolsNoColumn name in .var DataFrame that stores gene symbols.
stripplotNoAdd a stripplot on top of the violin plot.
jitterNoAdd jitter to the stripplot (only when stripplot is True).
sizeNoSize of the jitter points.
orderNoOrder in which to show the categories.
scaleNoThe method used to scale the width of each violin.width
swap_axesNoSwap axes such that observations are on the x-axis.

Implementation Reference

  • Generic handler function that executes all 'pl_' tools, including 'pl_stacked_violin', by mapping the tool name to sc.pl.stacked_violin and calling it with input arguments after validation.
    def run_pl_func(ads, func, arguments):
        """
        Execute a Scanpy plotting function with the given arguments.
        
        Parameters
        ----------
        adata : AnnData
            Annotated data matrix.
        func : str
            Name of the plotting function to execute.
        arguments : dict
            Arguments to pass to the plotting function.
            
        Returns
        -------
        The result of the plotting function.
        """
        adata = ads.adata_dic[ads.active]
        if func not in pl_func:
            raise ValueError(f"Unsupported function: {func}")
    
        run_func = pl_func[func]
        parameters = inspect.signature(run_func).parameters
        kwargs = {k: arguments.get(k) for k in parameters if k in arguments}    
    
        if "title" not in parameters:
            kwargs.pop("title", False)    
        kwargs.pop("return_fig", True)
        kwargs["show"] = False
        kwargs["save"] = ".png"
        try:
            fig = run_func(adata, **kwargs)
            fig_path = set_fig_path(func, **kwargs)
            add_op_log(adata, run_func, kwargs)
            return fig_path 
        except Exception as e:
            raise e
        return fig_path
  • Pydantic model defining the input schema and validation for the pl_stacked_violin tool.
    class StackedViolinModel(BaseStatPlotModel):
        """Input schema for the stacked_violin plotting tool."""
        
        stripplot: bool = Field(
            default=True,
            description="Add a stripplot on top of the violin plot."
        )
        
        jitter: Union[float, bool] = Field(
            default=True,
            description="Add jitter to the stripplot (only when stripplot is True)."
        )
        
        size: int = Field(
            default=1,
            description="Size of the jitter points.",
            gt=0
        )
        
        order: Optional[List[str]] = Field(
            default=None,
            description="Order in which to show the categories."
        )
        
        scale: Literal['area', 'count', 'width'] = Field(
            default='width',
            description="The method used to scale the width of each violin."
        )
        
        swap_axes: bool = Field(
            default=False,
            description="Swap axes such that observations are on the x-axis."
        )
        
        @field_validator('size')
        def validate_size(cls, v: int) -> int:
            """Validate size is positive"""
            if v <= 0:
                raise ValueError("size must be a positive integer")
            return v
  • Creates and configures the MCP Tool object for 'pl_stacked_violin' with schema reference.
    pl_stacked_violin = types.Tool(
        name="pl_stacked_violin",
        description="Plot stacked violin plots. Makes a compact image composed of individual violin plots stacked on top of each other.",
        inputSchema=StackedViolinModel.model_json_schema(),
    )
  • Maps the tool name 'pl_stacked_violin' to the underlying scanpy function sc.pl.stacked_violin for execution.
    pl_func = {
        "pl_pca": sc.pl.pca,
        "pl_embedding": sc.pl.embedding,  # Add the new embedding function
        "diffmap": sc.pl.diffmap,
        "pl_violin": sc.pl.violin,
        "pl_stacked_violin": sc.pl.stacked_violin,
        "pl_heatmap": sc.pl.heatmap,
        "pl_dotplot": sc.pl.dotplot,
        "pl_matrixplot": sc.pl.matrixplot,
        "pl_tracksplot": sc.pl.tracksplot,
        "pl_scatter": sc.pl.scatter,
        "embedding_density": sc.pl.embedding_density,
        "rank_genes_groups": sc.pl.rank_genes_groups,
        "pl_rank_genes_groups_dotplot": sc.pl.rank_genes_groups_dotplot,  # Add function mapping
        "pl_clustermap": sc.pl.clustermap,
        "pl_highly_variable_genes": sc.pl.highly_variable_genes,
        "pl_pca_variance_ratio": sc.pl.pca_variance_ratio,
    }
  • Registers the pl_stacked_violin Tool object in the pl_tools dictionary, which is exposed via server.list_tools().
    pl_tools = {
        "pl_pca": pl_pca_tool,
        "pl_embedding": pl_embedding,  # Add the new embedding tool
        # "diffmap": diffmap,
        "pl_violin": pl_violin,
        "pl_stacked_violin": pl_stacked_violin,
        "pl_heatmap": pl_heatmap,
        "pl_dotplot": pl_dotplot,
        "pl_matrixplot": pl_matrixplot,
        "pl_tracksplot": pl_tracksplot,
        "pl_scatter": pl_scatter,
        # "embedding_density": embedding_density,
        # "spatial": spatial,
        # "rank_genes_groups": rank_genes_groups,
        "pl_rank_genes_groups_dotplot": pl_rank_genes_groups_dotplot,  # Add tool mapping
        # "pl_clustermap": pl_clustermap,
        "pl_highly_variable_genes": pl_highly_variable_genes,
        "pl_pca_variance_ratio": pl_pca_variance_ratio,
    }
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. It states the tool creates a plot image, implying a read-only visualization operation, but doesn't disclose critical behavioral traits: whether it modifies input data, requires specific data formats (e.g., AnnData), handles errors, or produces output (e.g., file path, display). For a complex plotting tool with 22 parameters, this lack of context is a significant gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise and front-loaded: two sentences that directly state what the tool does and its key feature (compact stacking). Every word earns its place with zero redundancy. It efficiently communicates the core functionality without unnecessary elaboration.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given high complexity (22 parameters, no annotations, no output schema), the description is incomplete. It doesn't address what the tool returns (e.g., image file, plot object), data requirements, error conditions, or how it integrates with sibling tools. For a visualization tool in a data analysis context, more guidance on input data structure and output handling is needed.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all 22 parameters thoroughly. The description adds no parameter-specific information beyond the tool's general purpose. It doesn't explain key parameters like 'groupby' or 'var_names' in context, nor does it provide examples or typical usage patterns. Baseline 3 is appropriate when schema does all the work.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Plot stacked violin plots' specifies the verb and resource. It distinguishes from sibling 'pl_violin' by mentioning 'stacked on top of each other' and 'compact image', though not explicitly contrasting with other plotting tools. The purpose is specific but could better differentiate among multiple visualization siblings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus alternatives is provided. The description mentions 'compact image' which hints at a use case for space-efficient visualization, but it doesn't specify when stacked violins are preferable over regular violins ('pl_violin') or other plots like dot plots or heatmaps. No prerequisites, data requirements, or explicit alternatives are mentioned.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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