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list_available_models

Retrieve available models for a specified provider on the MCTS MCP Server to enable informed model selection for advanced topic analysis using Monte Carlo Tree Search algorithms.

Instructions

List available models for a provider

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
providerNoProvider namegemini

Implementation Reference

  • The core handler function that implements the 'list_available_models' tool logic. It returns a hardcoded list of available Gemini models based on the provider parameter.
    def list_available_models(provider: str = "gemini") -> dict[str, Any]:
        """
        List available models for a given provider.
    
        Args:
            provider: Provider name to list models for (currently only 'gemini' supported)
    
        Returns:
            Dict containing available models, default model, and current configuration
    
        Note:
            Model availability depends on the provider. Currently only Gemini models are supported.
        """
        if provider.lower() == "gemini":
            return {
                "provider": "gemini",
                "default_model": "gemini-2.0-flash-lite",
                "available_models": [
                    "gemini-2.0-flash-lite",
                    "gemini-2.0-flash-exp",
                    "gemini-1.5-pro",
                    "gemini-1.5-flash"
                ],
                "current_model": server_state["model"]
            }
        else:
            return {"error": f"Provider '{provider}' not supported", "available_providers": ["gemini"]}
  • Tool registration in the list_tools handler, defining the name, description, and input schema for 'list_available_models'.
    types.Tool(
        name="list_available_models",
        description="List available models for a provider",
        inputSchema={
            "type": "object",
            "properties": {
                "provider": {"type": "string", "description": "Provider name", "default": "gemini"}
            }
        }
    )
  • Dispatch logic in the main call_tool handler that routes calls to the list_available_models function.
    elif name == "list_available_models":
        result = list_available_models(**arguments)
  • Input schema definition for the list_available_models tool, specifying the provider parameter.
    inputSchema={
        "type": "object",
        "properties": {
            "provider": {"type": "string", "description": "Provider name", "default": "gemini"}
        }
    }
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 only states what the tool does, not how it behaves. It doesn't disclose whether this is a read-only operation, if it requires authentication, what format the list returns, or any rate limits or side effects. The description adds no behavioral context beyond the basic action.

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

Conciseness5/5

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

The description is a single, efficient sentence with zero wasted words. It's front-loaded with the core purpose and appropriately sized for a simple listing tool with one parameter.

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 tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what 'available models' means (e.g., model names, capabilities, statuses), how results are structured, or any dependencies on other tools like 'set_provider'. The agent lacks critical context to use this effectively.

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

Parameters3/5

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

The schema has 100% description coverage, with the single parameter 'provider' documented as 'Provider name' with a default value. The description adds no additional meaning about parameters beyond implying the tool filters by provider, which is already clear from the schema. Baseline 3 is appropriate when the schema does the heavy lifting.

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 action ('List') and resource ('available models for a provider'), making the purpose immediately understandable. It doesn't distinguish from sibling tools like 'get_status' or 'set_provider', but the verb+resource combination is specific enough for basic understanding.

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. The description doesn't mention prerequisites, appropriate contexts, or relationships to sibling tools like 'set_provider' (which might affect available models) or 'get_synthesis' (which might use these models).

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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