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list_models

Discover available AI models with descriptions and recommended use cases to select the right model for your specific task requirements.

Instructions

List all available AI models with their descriptions and best use cases

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The primary handler function for the 'list_models' MCP tool. It retrieves the list of models from the ConsultationService and returns them formatted as a JSON string in a ToolResponse.
    private handleListModels(): ToolResponse {
      if (this.config.verboseLogging) {
        console.error("[MCP] Fetching available models list");
      }
    
      const models = this.consultationService.listModels();
    
      if (this.config.verboseLogging) {
        console.error(`[MCP] Found ${models.length} available models`);
      }
    
      return {
        content: [
          {
            type: "text" as const,
            text: JSON.stringify(models, null, 2),
          },
        ],
      };
    }
  • Registers the 'list_models' tool in the MCP server, including its name, description, and input schema (no required parameters).
    {
      name: "list_models",
      description:
        "List all available AI models with their descriptions and best use cases",
      inputSchema: {
        type: "object",
        properties: {},
      },
    },
  • Defines the input schema for the 'list_models' tool, which requires no parameters.
    inputSchema: {
      type: "object",
      properties: {},
    },
  • Helper method in ConsultationService that fetches and formats the list of available models from ModelSelector, invoked by the tool handler.
    public listModels() {
      const models = this.modelSelector.getAllModels();
      return Object.entries(models).map(([key, model]) => ({
        name: key,
        id: model.id,
        description: model.description,
        bestFor: model.bestFor,
      }));
    }
  • Dispatch case in the main handleToolCall method that routes 'list_models' tool calls to the specific handler.
    case "list_models":
      result = this.handleListModels();
      break;
Behavior2/5

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 mentions what data is returned (descriptions and use cases) but doesn't cover critical aspects like whether this is a read-only operation, potential rate limits, authentication needs, or response format. For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.

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 that directly states the tool's purpose without any redundant or unnecessary words. It is front-loaded with the core action and resource, making it highly concise and well-structured.

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

Completeness3/5

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

Given the tool's simplicity (0 parameters, no output schema, no annotations), the description is adequate as a basic overview. However, it lacks details on behavioral traits and usage context, which are needed for full completeness, especially without annotations to fill those gaps.

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, and the schema description coverage is 100%, so there are no parameters to document. The description appropriately doesn't discuss parameters, which is correct for this case, earning a high score as it doesn't need to compensate for any gaps.

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 verb ('List') and resource ('all available AI models'), specifying what information is returned ('with their descriptions and best use cases'). However, it doesn't differentiate from the sibling tool 'consult_ai', which might be a related AI interaction tool, so it doesn't reach the highest score.

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?

The description provides no guidance on when to use this tool versus the sibling 'consult_ai' or any alternatives. It lacks context about prerequisites, timing, or exclusions, offering only a basic statement of function without usage instructions.

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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