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Enkrypt AI MCP Server

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remove_model

Delete a specified model by its saved name using the Enkrypt AI MCP Server. Returns a response with details of the deleted model for verification.

Instructions

Remove a model.

Args: test_model_saved_name: The saved name of the model to be removed.

Returns: A dictionary containing the response message and details of the deleted model.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
test_model_saved_nameYes

Implementation Reference

  • The handler function for the 'remove_model' tool. It takes the saved name of the model, calls model_client.delete_model to remove it, and returns the response as a dictionary. The @mcp.tool() decorator registers it as an MCP tool.
    @mcp.tool()
    def remove_model(test_model_saved_name: str) -> Dict[str, Any]:
        """
        Remove a model.
    
        Args:
            test_model_saved_name: The saved name of the model to be removed.
    
        Returns:
            A dictionary containing the response message and details of the deleted model.
        """
        # Remove the model
        delete_response = model_client.delete_model(model_saved_name=test_model_saved_name)
    
        # Print as a dictionary
        return delete_response.to_dict()
Behavior2/5

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

With no annotations provided, the description carries full burden but provides minimal behavioral information. It states this is a removal operation (implying destructive action) and mentions the return format, but doesn't disclose important behavioral traits like whether removal is permanent, what permissions are required, whether it affects associated resources, or any rate limits. The description doesn't contradict annotations since none exist, but it's insufficient for a destructive operation.

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 concise with three brief sections (purpose, args, returns). Each sentence serves a clear purpose with no wasted words. The structure is logical and front-loaded with the main action. It could be slightly more informative while maintaining conciseness.

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?

For a destructive operation with no annotations, no output schema, and minimal parameter documentation, the description is incomplete. It doesn't explain what 'removing a model' entails operationally, what happens to dependent resources, whether the action is reversible, or what the response dictionary contains. Given the complexity of model management and the presence of sibling tools like 'modify_model_config', more context is needed.

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

Parameters2/5

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

Schema description coverage is 0% (parameter has no description in schema), and the description only states 'The saved name of the model to be removed' without explaining what a 'saved name' is, how it differs from other identifiers, or where to find this information. For a single parameter with zero schema documentation, this minimal explanation doesn't adequately compensate for the coverage gap.

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

Purpose3/5

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

The description states the tool 'Remove a model' which is a clear verb+resource combination, but it doesn't differentiate from sibling tools like 'remove_deployment' or 'remove_guardrails_policy' that follow the same pattern. The purpose is understandable but lacks specificity about what type of model is being removed or how it differs from other removal operations.

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 is provided about when to use this tool versus alternatives. There's no mention of prerequisites, what happens after removal, or when not to use it. The description doesn't reference sibling tools like 'list_models' that might be needed first or 'add_model' as a complementary operation.

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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