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Enkrypt AI MCP Server

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by enkryptai

list_models

Retrieve and display a list of available AI models, including detailed information about the first model, for analysis and integration within the Enkrypt AI MCP Server.

Instructions

List all models and print details of the first model.

Returns: A dictionary containing the list of models.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The core handler function for the 'list_models' MCP tool. It is registered via the @mcp.tool() decorator and retrieves the list of all models using model_client.get_model_list(), returning them as a dictionary.
    @mcp.tool()
    def list_models() -> Dict[str, Any]:
        """
        List all models and print details of the first model.
    
        Returns:
            A dictionary containing the list of models.
        """
        # List all models
        models = model_client.get_model_list()
    
        # Return the list of models as a dictionary
        return models.to_dict()
Behavior2/5

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

With no annotations, the description carries full burden but provides minimal behavioral insight. It mentions returning a dictionary but doesn't disclose format, pagination, rate limits, permissions needed, or what 'print details' entails operationally. This is inadequate for a tool with zero annotation coverage.

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

Conciseness3/5

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

The description is brief but inefficiently structured. The first sentence mixes listing and printing actions, and the 'Returns' section is redundant since it restates the obvious. It could be more front-loaded and concise without losing clarity.

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?

Given no annotations, no output schema, and a description that lacks detail on behavior, format, or differentiation from siblings, this is incomplete. For a list operation among many sibling tools, more context on what 'models' entails and the return structure is needed.

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 with 100% schema description coverage, so no parameter documentation is needed. The description doesn't add parameter semantics, but this is acceptable given the lack of parameters, aligning with the baseline expectation for zero-param tools.

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 'List all models' which provides a clear verb+resource, but it's vague about what 'print details of the first model' means in the context of returning a dictionary. It doesn't distinguish from siblings like 'get_model_details' or 'list_deployments', leaving ambiguity about scope and differentiation.

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 on when to use this tool versus alternatives like 'get_model_details' or 'add_model'. The description implies a listing function but doesn't specify context, prerequisites, or exclusions, leaving the agent without usage direction.

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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