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Noctua MCP Server

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remove_individual

Delete an individual from a GO-CAM model, including all connected facts and edges, to maintain accurate biological knowledge representation.

Instructions

Remove an individual from a GO-CAM model.

Note: This will also remove all facts (edges) connected to this individual.

Args: model_id: The GO-CAM model identifier individual_id: The individual to remove

Returns: Barista API response

Examples: # Remove using a variable reference (within same batch) remove_individual("gomodel:12345", "mf1")

# Remove using full individual ID
remove_individual("gomodel:12345", "gomodel:12345/5fce9b7300001215")

# Remove an evidence individual
remove_individual("gomodel:12345", "gomodel:12345/evidence_123")

# Clean up after testing
for ind_id in ["test1", "test2", "test3"]:
    remove_individual("gomodel:12345", ind_id)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
model_idYes
individual_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The main handler function for the 'remove_individual' MCP tool. It calls BaristaClient.remove_individual to delete the specified individual from the GO-CAM model, handling validation errors and rollbacks, and returns a success/error response.
    @mcp.tool()
    async def remove_individual(
        model_id: str,
        individual_id: str
    ) -> Dict[str, Any]:
        """
        Remove an individual from a GO-CAM model.
    
        Note: This will also remove all facts (edges) connected to this individual.
    
        Args:
            model_id: The GO-CAM model identifier
            individual_id: The individual to remove
    
        Returns:
            Barista API response
    
        Examples:
            # Remove using a variable reference (within same batch)
            remove_individual("gomodel:12345", "mf1")
    
            # Remove using full individual ID
            remove_individual("gomodel:12345", "gomodel:12345/5fce9b7300001215")
    
            # Remove an evidence individual
            remove_individual("gomodel:12345", "gomodel:12345/evidence_123")
    
            # Clean up after testing
            for ind_id in ["test1", "test2", "test3"]:
                remove_individual("gomodel:12345", ind_id)
        """
        client = get_client()
        resp = client.remove_individual(model_id, individual_id)
    
        if resp.validation_failed:
            return {
                "success": False,
                "error": "Validation failed",
                "reason": resp.validation_reason,
                "rolled_back": True,
                "individual_id": individual_id,
                "model_id": model_id
            }
    
        if resp.error:
            return {
                "success": False,
                "error": resp.error,
                "individual_id": individual_id,
                "model_id": model_id
            }
    
        # Return minimal success response
        return {
            "success": True,
            "removed": True,
            "individual_id": individual_id
        }
Behavior4/5

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

With no annotations provided, the description carries full burden and does well by disclosing critical behavioral traits: it explicitly warns that removal 'will also remove all facts (edges) connected to this individual,' revealing cascading deletion behavior. It also shows through examples that it accepts different ID formats and can be used in batch operations, though it doesn't mention permissions, rate limits, or error conditions.

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 and well-structured with a clear purpose statement, important note about cascading effects, parameter explanations, return value indication, and multiple practical examples. While comprehensive, every section adds value, though it could be slightly more front-loaded with key information.

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

Completeness4/5

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

Given this is a destructive mutation tool with no annotations but with output schema (mentioned in Returns), the description provides good context: explains the cascading deletion behavior, documents both parameters thoroughly, and shows multiple usage examples. It doesn't cover all edge cases or error scenarios, but for a 2-parameter tool with output schema, it's quite complete.

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

Parameters5/5

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

With 0% schema description coverage, the description fully compensates by clearly explaining both parameters: 'model_id: The GO-CAM model identifier' and 'individual_id: The individual to remove.' The examples further illustrate valid values for both parameters, adding significant meaning beyond the bare schema.

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

Purpose5/5

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

The description clearly states the specific action ('Remove an individual from a GO-CAM model') and distinguishes it from siblings like 'remove_fact' by focusing on individuals rather than facts. It specifies the resource being operated on (individual in a GO-CAM model).

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

Usage Guidelines3/5

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

The description implies usage through examples showing removal of various individual types (variable references, full IDs, evidence individuals, test cleanup), but doesn't explicitly state when to use this tool versus alternatives like 'remove_fact' or provide clear prerequisites. The examples suggest context but lack explicit guidance.

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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