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listReasoningModels

Retrieve a list of reasoning-capable AI models supported by the MindBridge MCP Server, enabling dynamic selection for diverse tasks across multiple providers.

Instructions

List all available models that support reasoning capabilities

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The handler function for the 'listReasoningModels' tool. It returns a JSON string containing the list of reasoning-optimized models from the REASONING_MODELS constant.
    async () => {
      try {
        return {
          content: [{
            type: 'text',
            text: JSON.stringify({
              models: REASONING_MODELS,
              description: 'These models are specifically optimized for reasoning tasks and support the reasoning_effort parameter.'
            }, null, 2)
          }]
        };
      } catch (error) {
        return {
          content: [{ type: 'text', text: `Error: ${error instanceof Error ? error.message : 'An unknown error occurred'}` }],
          isError: true
        };
      }
    }
  • src/server.ts:113-135 (registration)
    Registration of the 'listReasoningModels' tool in the MindBridgeServer class using this.tool(), including description, empty input schema, and inline handler.
    // Register listReasoningModels tool
    this.tool('listReasoningModels',
      'List all available models that support reasoning capabilities',
      {},
      async () => {
        try {
          return {
            content: [{
              type: 'text',
              text: JSON.stringify({
                models: REASONING_MODELS,
                description: 'These models are specifically optimized for reasoning tasks and support the reasoning_effort parameter.'
              }, null, 2)
            }]
          };
        } catch (error) {
          return {
            content: [{ type: 'text', text: `Error: ${error instanceof Error ? error.message : 'An unknown error occurred'}` }],
            isError: true
          };
        }
      }
    );
  • The REASONING_MODELS constant, an array of model names optimized for reasoning tasks, used by the tool handler.
    // Export available reasoning models
    export const REASONING_MODELS = [
      MODEL_NAMES.OPENAI.O1,
      MODEL_NAMES.OPENAI.O3_MINI,
      MODEL_NAMES.DEEPSEEK.REASONER,
      MODEL_NAMES.ANTHROPIC.CLAUDE_37_SONNET
    ] as const;
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 states what the tool does but doesn't describe how it behaves - no information about pagination, rate limits, authentication needs, return format, or error conditions. For a tool with zero annotation coverage, this leaves significant behavioral gaps.

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 states exactly what the tool does with zero wasted words. It's appropriately sized for a simple list operation and front-loads the core functionality immediately.

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?

For a simple list operation with no parameters and no output schema, the description provides the basic purpose but lacks important context. Without annotations or output schema, the description should ideally mention what information is returned about each model, but it doesn't. It's minimally adequate but leaves the agent guessing about the response format.

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 the schema already fully documents the parameter situation. The description appropriately doesn't discuss parameters since none exist. This meets the baseline expectation for a parameterless tool.

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 models that support reasoning capabilities'), making the purpose immediately understandable. It doesn't specifically differentiate from sibling tools like 'listProviders' or 'getSecondOpinion', but the focus on 'reasoning capabilities' provides some distinction. This is clear but lacks explicit sibling 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?

The description provides no guidance on when to use this tool versus alternatives like 'listProviders' or 'getSecondOpinion'. There's no mention of prerequisites, context, or exclusions. The agent must infer usage based solely on the tool name and description without explicit 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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