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remove_fact

Delete specific facts from GO-CAM biological models by specifying exact subject-predicate-object triples to maintain accurate knowledge representation.

Instructions

Remove a fact from a GO-CAM model.

You must specify the exact triple (subject, predicate, object) to remove.

Args: model_id: The GO-CAM model identifier subject_id: Subject of the fact object_id: Object of the fact predicate_id: Predicate of the fact

Returns: Barista API response

Examples: # Remove an enabled_by relationship remove_fact( "gomodel:12345", "gomodel:12345/mf_123", "gomodel:12345/gp_456", "RO:0002333" )

# Remove a causal relationship
remove_fact(
    "gomodel:12345",
    "gomodel:12345/activity1",
    "gomodel:12345/activity2",
    "RO:0002413"  # provides input for
)

# Remove occurs_in relationship
remove_fact(
    "gomodel:12345",
    "gomodel:12345/mf_123",
    "gomodel:12345/cc_789",
    "BFO:0000066"  # occurs_in
)

# Remove using variable references (within same batch)
remove_fact("gomodel:12345", "mf1", "gp1", "RO:0002333")

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
model_idYes
subject_idYes
object_idYes
predicate_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The handler function for the 'remove_fact' MCP tool. It validates the removal of a specific fact (subject-predicate-object triple) from a GO-CAM model using the BaristaClient and returns success/error status with details.
    @mcp.tool()
    async def remove_fact(
        model_id: str,
        subject_id: str,
        object_id: str,
        predicate_id: str
    ) -> Dict[str, Any]:
        """
        Remove a fact from a GO-CAM model.
    
        You must specify the exact triple (subject, predicate, object) to remove.
    
        Args:
            model_id: The GO-CAM model identifier
            subject_id: Subject of the fact
            object_id: Object of the fact
            predicate_id: Predicate of the fact
    
        Returns:
            Barista API response
    
        Examples:
            # Remove an enabled_by relationship
            remove_fact(
                "gomodel:12345",
                "gomodel:12345/mf_123",
                "gomodel:12345/gp_456",
                "RO:0002333"
            )
    
            # Remove a causal relationship
            remove_fact(
                "gomodel:12345",
                "gomodel:12345/activity1",
                "gomodel:12345/activity2",
                "RO:0002413"  # provides input for
            )
    
            # Remove occurs_in relationship
            remove_fact(
                "gomodel:12345",
                "gomodel:12345/mf_123",
                "gomodel:12345/cc_789",
                "BFO:0000066"  # occurs_in
            )
    
            # Remove using variable references (within same batch)
            remove_fact("gomodel:12345", "mf1", "gp1", "RO:0002333")
        """
        client = get_client()
        resp = client.remove_fact(model_id, subject_id, object_id, predicate_id)
    
        if resp.validation_failed:
            return {
                "success": False,
                "error": "Validation failed",
                "reason": resp.validation_reason,
                "rolled_back": True,
                "fact": {
                    "subject": subject_id,
                    "predicate": predicate_id,
                    "object": object_id
                },
                "model_id": model_id
            }
    
        if resp.error:
            return {
                "success": False,
                "error": resp.error,
                "fact": {
                    "subject": subject_id,
                    "predicate": predicate_id,
                    "object": object_id
                },
                "model_id": model_id
            }
    
        # Return minimal success response
        return {
            "success": True,
            "removed": True
        }
Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It clearly indicates this is a destructive operation ('Remove'), but doesn't specify permissions needed, whether the removal is reversible, rate limits, or what happens to dependent facts. The examples show different use cases, adding some practical context about what can be removed.

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 front-loaded with the core purpose. The examples section is extensive but serves a clear pedagogical purpose. The structure moves from general description to specific args documentation to practical examples, though the examples section could be slightly more concise.

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, the description does a good job explaining the core functionality. The presence of an output schema means the description doesn't need to explain return values. However, for a tool that permanently removes data, additional warnings about irreversible changes or permission requirements would improve completeness.

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, the description must fully compensate. It provides a clear explanation of all 4 parameters ('subject, predicate, object' triple plus model_id) and includes multiple examples showing how each parameter is used in practice. The examples demonstrate different predicate types and show both literal IDs and variable references, adding significant semantic value 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 a fact') and resource ('from a GO-CAM model'), distinguishing it from sibling tools like 'remove_individual' or 'add_fact'. It provides a precise verb+resource combination that leaves no ambiguity about its function.

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

Usage Guidelines4/5

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

The description provides clear context about when to use this tool ('You must specify the exact triple to remove'), but doesn't explicitly mention when NOT to use it or name specific alternatives. It implies usage for removing facts rather than individuals or other entities, but lacks explicit exclusions or comparisons to sibling tools.

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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