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list_var

Lists key column names in adata.var for single-cell RNA sequencing analysis, enabling users to identify required inputs for other SCMCP tools.

Instructions

list key columns in adata.var. it should be called for checking when other tools need var key column names input

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The core handler function for the 'list_var' tool. It takes an AnnData object and returns the list of column names in adata.var.
    def list_var(adata):
        return list(adata.var.columns)
  • Pydantic model used as the input schema for the 'list_var' tool. Being empty (pass), it indicates no input parameters are required.
    class ListVarModel(JSONParsingModel):
        """ListVarModel"""    
        pass
  • Definition and registration of the MCP Tool object named 'list_var', linking the handler via schema.
    list_var_tool = types.Tool(
        name="list_var",
        description="list key columns in adata.var. it should be called for checking when other tools need var key column names input",
        inputSchema=ListVarModel.model_json_schema(),
    )
  • 'list_var_tool' is registered in the util_tools dictionary, which is exposed to the MCP server for listing and calling.
    util_tools = {
        "mark_var": mark_var_tool,
        "list_var": list_var_tool,
        "list_obs": list_obs_tool,  
        "check_gene": check_gene_tool,
        "merge_adata": merge_adata_tool,
    }
  • Dispatch logic in the MCP server's call_tool handler that routes 'list_var' calls to run_util_func when name is in util_tools.
    elif name in util_tools.keys():            
        res = run_util_func(ads, name, arguments)
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. The description mentions it's for 'checking' and listing, which implies a read-only operation, but doesn't specify if it requires specific permissions, what the output format is, or any rate limits. For a tool with zero annotation coverage, this leaves significant behavioral gaps, though it's not misleading.

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 two concise sentences that are front-loaded with the core purpose and followed by usage guidance. Every sentence adds value without redundancy, making it efficient and well-structured for an AI agent to parse quickly.

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

Completeness3/5

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

Given the tool has 0 parameters, no annotations, and no output schema, the description adequately covers the purpose and usage. However, it lacks details on behavioral aspects like output format or error handling, which could be important for a tool used as a prerequisite in workflows. It's minimally viable but has clear gaps in context.

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

Parameters4/5

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

The tool has 0 parameters, and the input schema has 100% description coverage (though the schema description 'ListVarModel' is minimal). With no parameters, the baseline score is 4, as there's nothing for the description to add beyond what's already covered. The description doesn't need to explain parameters, so it meets expectations.

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: 'list key columns in adata.var' with a specific verb ('list') and resource ('key columns in adata.var'). It distinguishes from siblings like 'list_obs' which handles a different data structure, though it doesn't explicitly mention this distinction. The purpose is clear but could be more specific about what 'key columns' means.

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

Usage Guidelines5/5

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

The description provides explicit usage guidance: 'it should be called for checking when other tools need var key column names input.' This clearly states when to use this tool (as a prerequisite for other tools requiring var key column names) and implies when not to use it (for other purposes). It effectively guides the agent on its role in the workflow.

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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