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get_organ

Retrieve multi-agent workflow templates for complex tasks requiring collaborative reasoning and multi-perspective analysis.

Instructions

Returns an organ template for multi-agent orchestration (Layer 4).

Organs combine programs and cells into cohesive workflows for complex tasks
requiring multi-perspective analysis or collaborative reasoning.

Args:
    name: Identifier of the organ ('debate_council' for multi-perspective debate).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNodebate_council

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • MCP tool registration decorator for get_organ tool.
    @mcp.tool()
  • Handler function for the 'get_organ' MCP tool. Validates input using OrganInput and delegates to get_organ_template.
    @mcp.tool()
    def get_organ(name: str = "debate_council") -> str:
        """
        Returns an organ template for multi-agent orchestration (Layer 4).
    
        Organs combine programs and cells into cohesive workflows for complex tasks
        requiring multi-perspective analysis or collaborative reasoning.
    
        Args:
            name: Identifier of the organ ('debate_council' for multi-perspective debate).
        """
        try:
            model = OrganInput(name=name)
        except ValidationError as e:
            return f"Input Validation Error: {e}"
    
        return get_organ_template(model.name)
  • Pydantic schema used for input validation in get_organ handler.
    class OrganInput(BaseModel):
        name: str = Field("debate_council", min_length=1, description="Organ name.")
  • Core helper function that implements the logic to retrieve specific organ templates based on normalized organ_name.
    def get_organ_template(organ_name: str) -> str:
        """Return an organ template for orchestrating multi-agent workflows.
    
        Args:
            organ_name: Identifier for the organ (e.g., "debate_council").
    
        Returns:
            Template string for the requested organ, or error message if not found.
        """
        normalized_name = (
            organ_name.lower().replace("organ.", "").replace("_", "").replace("-", "")
        )
    
        # Match debate_council variants
        if normalized_name in ["debatecouncil", "debate", "multiperspective"]:
            return ORGAN_DEBATE_COUNCIL
    
        # Match research_synthesis variants
        if normalized_name in ["researchsynthesis", "research", "scoutarchitectscribe"]:
            return ORGAN_RESEARCH_SYNTHESIS
    
        # Match tool_master variants
        if normalized_name in ["toolmaster", "tool", "master", "meta"]:
            return ORGAN_TOOL_MASTER
    
        # Return helpful error for unknown organs
        available = ["debate_council", "research_synthesis", "tool_master"]
        return (
            f"// Organ '{organ_name}' not found.\\n"
            f"// Available organs: {', '.join(available)}\\n"
            f"// Returning debate_council as example:\\n\\n" + ORGAN_DEBATE_COUNCIL
        )
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. While it states this is a 'returns' operation (implying read-only), it doesn't address important behavioral aspects like whether this requires authentication, what happens if the organ doesn't exist, whether there are rate limits, or what the output format looks like. The description adds some context about what organs are but lacks operational transparency.

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

Conciseness4/5

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

The description is appropriately sized with three sentences that each serve a purpose: stating what the tool returns, explaining what organs are, and documenting the parameter. It's front-loaded with the core functionality. The only minor inefficiency is the parenthetical '(Layer 4)' which might be jargon without explanation.

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 that there's an output schema (which handles return values) and only one parameter with 0% schema coverage, the description provides adequate basic information but could be more complete. It explains what organs are and documents the parameter, but for a tool in a complex multi-agent orchestration context with many sibling tools, more guidance about when to use this specific tool would be helpful.

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?

With 0% schema description coverage and only one parameter, the description compensates well by explaining that 'name' is an 'Identifier of the organ' and provides an example value ('debate_council' for multi-perspective debate). This adds meaningful context beyond what the bare schema provides, though it doesn't specify whether other organ names exist or what format they should follow.

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 'returns an organ template for multi-agent orchestration (Layer 4)' and explains that organs combine programs and cells into workflows for complex tasks. This provides a specific verb ('returns') and resource ('organ template') with context about its role in multi-agent systems. However, it doesn't explicitly differentiate from sibling tools like 'get_cell_protocol' or 'get_molecular_template' which might return similar templates.

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?

The description provides some context about when organs are used ('for complex tasks requiring multi-perspective analysis or collaborative reasoning'), but it doesn't offer explicit guidance on when to use this tool versus alternatives like 'get_cell_protocol' or 'get_molecular_template' from the sibling list. There's no mention of prerequisites, exclusions, or specific scenarios where this tool is preferred over others.

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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