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create_persona

Create conversational AI personas by configuring speech, language, and perception settings for video generation and replica management.

Instructions

Create a new persona for conversational AI

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
persona_nameNoName for the persona
replica_idNoReplica to use for this persona
contextNoContextual information for the LLM
system_promptNoSystem prompt for the LLM
layersNoConfiguration layers for the persona

Implementation Reference

  • The createPersona method is the handler that executes the create_persona tool logic. It makes a POST request to /personas endpoint with the provided arguments and returns the response data as formatted JSON text.
    private async createPersona(args: any) {
      const response = await this.axiosInstance.post('/personas', args);
      return {
        content: [{
          type: 'text',
          text: JSON.stringify(response.data, null, 2),
        }],
      };
    }
  • Tool registration with name 'create_persona', description 'Create a new persona for conversational AI', and input schema defining properties: persona_name, replica_id, context, system_prompt, and layers (with nested stt, llm, tts, perception settings).
    // Replica Personas
    {
      name: 'create_persona',
      description: 'Create a new persona for conversational AI',
      inputSchema: {
        type: 'object',
        properties: {
          persona_name: {
            type: 'string',
            description: 'Name for the persona',
          },
          replica_id: {
            type: 'string',
            description: 'Replica to use for this persona',
          },
          context: {
            type: 'string',
            description: 'Contextual information for the LLM',
          },
          system_prompt: {
            type: 'string',
            description: 'System prompt for the LLM',
          },
          layers: {
            type: 'object',
            description: 'Configuration layers for the persona',
            properties: {
              stt: { type: 'object', description: 'Speech-to-text settings' },
              llm: { type: 'object', description: 'Language model settings' },
              tts: { type: 'object', description: 'Text-to-speech settings' },
              perception: { type: 'object', description: 'Perception settings (Raven-0)' },
            },
          },
        },
      },
    },
  • src/index.ts:728-729 (registration)
    Switch case statement that routes 'create_persona' tool calls to the createPersona handler method.
    case 'create_persona':
      return await this.createPersona(request.params.arguments);
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It states this is a creation operation, implying it's a write/mutation tool, but doesn't disclose any behavioral traits like required permissions, whether it's idempotent, what happens on duplicate names, rate limits, or what the response contains. For a creation tool with zero annotation coverage, this leaves significant gaps in understanding how it behaves.

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, clear sentence that directly states the tool's purpose with zero wasted words. It's appropriately sized for a creation tool and front-loads the essential information without unnecessary elaboration.

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 creation tool with 5 parameters (including nested objects), no annotations, and no output schema, the description is insufficiently complete. It doesn't address what happens after creation, error conditions, or behavioral aspects like whether 'replica_id' must reference an existing replica. The agent lacks context about the tool's full operation despite the good schema coverage.

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 description adds no parameter-specific information beyond what's already in the schema, which has 100% coverage with detailed descriptions for all 5 parameters. The baseline score of 3 reflects that the schema adequately documents parameters, so the description doesn't need to compensate, but it also doesn't provide additional context like parameter relationships or usage examples.

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 ('Create') and resource ('new persona for conversational AI'), making the purpose immediately understandable. It distinguishes this from other tools like 'create_conversation' or 'create_replica' by specifying it's for personas. However, it doesn't explicitly contrast with sibling tools like 'patch_persona' or 'get_persona' in the description text itself.

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. It doesn't mention prerequisites (e.g., needing an existing replica), when not to use it, or how it differs from similar tools like 'patch_persona' for updates. The agent must infer usage from the tool name and context alone.

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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